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Purpose: Spontaneous speech tasks are critically important for characterizing spoken language production deficits in aphasia and for assessing the impact of therapy. The utility of such tasks arises from the complex interaction of linguistic demands (word retrieval, sentence formulation, articulation). However, this complexity also makes spontaneous speech hugely variable and difficult to assess. The current study aimed to simplify the problem by identifying latent factors underlying performance in spontaneous speech in aphasia. The ecological validity of the factors was examined by examining how well the factor structures corresponded to traditionally defined aphasia subtypes. Method: A factor analysis was conducted on 17 microlinguistic measures of narratives from 274 individuals with aphasia in AphasiaBank. The resulting factor scores were compared across aphasia subtypes. Supervised (linear discriminant analysis) and unsupervised (latent profile analysis) classification techniques were then conducted on the factor scores and the solutions compared to traditional aphasia subtypes. Results: Six factors were identified. Two reflected aspects of fluency, one at the phrase level (Phrase Building) and one at the narrative level (Narrative Productivity). Two other factors reflected the accuracy of productions, one at the word level (Semantic Anomaly) and one at the utterance level (Grammatical Error). The other two factors reflected the complexity of sentence structures (Grammatical Complexity) and the use of repair behaviors (Repair), respectively. Linear discriminant analyses showed that only about two thirds of speakers were classified correctly and that misclassifications were similar to disagreements between clinical diagnoses. The most accurately diagnosed syndromes were the largest groups--Broca's and anomic aphasia. The latent profile analysis also generated profiles similar to Broca's and anomic aphasia but separated some subtypes according to severity. Conclusions: The factor solution and the classification analyses reflected broad patterns of spontaneous speech performance in a large and representative sample of individuals with aphasia. However, such data-driven approaches present a simplified picture of aphasia patterns, much as traditional syndrome categories do. To ensure ecological validity, a hybrid approach is recommended, balancing population-level analyses with examination of performance at the level of theoretically specified subgroups or individuals. Supplemental Material: https://doi.org/10.23641/asha. 13232354, 'Spontaneous speech' refers to spoken language production of a message intended and generated by the speaker. In contrast to repetition and oral reading, spontaneous speech tasks require the speaker to [...]

A mathematical model is proposed for revealing the absence of a compound to be identified in an electron impact mass spectral library. The mathematical model (developed based on PLS Discriminant Analysis) can be represented as a 'black box' which provides an answer whether a compound to be sought is absent or present in a database. The match factors of top ten candidates among the possible ones were used as input data. More than 5000 objects (mass spectra) were used at the steps of training, validation, and testing. The developed classification model provides correct prediction (of whether a compound is absent from the library) in 28.4% cases, while only 1.2% of compounds present in the database were incorrectly classified as the absent ones. Keywords: identification of organic compounds, GC/MS, mass spectral library, mass spectral database, classification, PLS discriminant analysis DOI: 10.1134/S1061934817140143, INTRODUCTION Combined gas chromatography--mass spectrometry (GC/MS) is one of the most widely used methods for the trace determination of organic compounds in samples of complex composition. The possibilities of this [...]

Item response modeling (IRM/IRT) has been known to marketing scholars for a number of years. However, with the exception of some notable and important applications in international (cross-cultural) marketing and consumer behavior, even a cursory reading of marketing journals reveals a general lack of interest in applying IRM, despite its ability to provide highly useful measurement-related information. To address and hopefully remedy the paucity of adoption, we offer an application-oriented discussion of the utility of IRM for marketing and related business research to enable researchers to utilize the strengths and realize the benefits of this methodology in their empirical work. After a short discussion of the history of IRM, we focus on its fundamentals within a modern statistical framework based on the generalized linear model and closely related non-linear factor analysis. We then engage major concepts of IRM, including item characteristic curve, local independence, and dimensionality, as well as parameter estimation and information functions. The popular one- and two-parameter logistic models are next discussed, as is the issue of model selection. Several polytomous item response models are subsequently dealt with, followed by a discussion of multidimensional IRM and data illustrations of item response models using widely available software. References to exemplar marketing applications are provided along the way, and a discussion of limitations of IRM concludes the article. Keywords Dimensionality * Factor analysis * Generalized linear model * Item characteristic curve * Item response modeling * Latent variable modeling * Local independence * Non-linear factor analysis, The utility of item response modeling in marketing research Marketing and business researchers are becoming increasingly interested in investigating theoretical concepts that are not directly observable, such as consumer satisfaction, [...]

Current data suggest that lobster (Homarus americanus) populations are less homogenous than once believed. In an effort to better discriminate morphologically among lobsters from different sites, we developed a photographic method using ImageJ and compared it with commonly used 'hand' measurements. We standardized the measuring process using a strap-down board for both dorsal and ventral photographs with a camera mounted at a fixed position above the lobster. Discriminant analysis showed that both hand and photographic methods were useful in discriminating lobsters--both males and females--from three different sites. Additionally, the photographic method improved reproducibility and resolution, it reduced measurement time at the dock, and it created a permanent record for later verification, additional statistical analyses, and observer training. Les donnees actuellement disponibles portent a croire que les populations de homards (Homarus americanus) sont moins homogenes que l'on ne le pensait. Afin d'en arriver a une meilleure discrimination morphologique de homards provenant de lieux differents, nous avons mis au point une methode photographique appelee ImageJ et l'avons comparee a des approaches <> de mesure couramment employees. Nous avons normalise le processus de mesure en utilisant une planche munie d'attaches pour la prise de photographies dorsales et ventrales a l'aide d'un appareil-photo installe a hauteur fixe au-dessus du homard. L'analyse discriminante a demontre l'utilite des methodes tant manuelle que photographique pour la discrimination de homards tant males que femelles provenant de trois sites differents. La methode photographique a en outre ameliore la reproductibilite et la resolution, reduit le temps de mesure au quai et produit un registre permanent pouvant etre utilise pour des verifications ulterieures, d'autres analyses statistiques et la formation d'observateurs. [Traduit par la Redaction], Introduction The phenotype of an organism can provide insight into its habitat and behavior. Morphometrics, the analysis of shape, applies multivariate methods to lengths, widths, and angles. Morphometric analyses using [...]

A 20 item observational measure of social functioning, the Impression of Interviewee rating scale, is one of three measures devised to assess the broader autism phenotype. The sample studied included families containing at least two individuals with autism spectrum disorder; observations were undertaken by the researcher who interviewed the subject. An exploratory factor analysis suggested a single factor was most appropriate (Cronbach's [alpha] of 0.78). There was a modest but significant retest correlation of 0.42. Correlations between live ratings and blind consensus ratings of vignettes were high (0.93). Correlations with the interview measures were moderate but statistically significant. In conclusion, the observational scale provides a promising start but further work is required before general use can be recommended. Keywords Broader autism phenotype * Observer rating * Test-retest * Factor analysis * Social functioning, Introduction The broader autism phenotype (BAP) primarily comprises socio-communicative features that should be observable in the course of ordinary social interactions. The International Molecular Study of Autism Consortium (IMGSAC) developed [...]

Abstract Purpose--The Islamic banking sector has been growing rapidly, particularly in the Gulf region. The purpose of this paper is to examine major barriers to service quality in Islamic banks [...]

A study presents a business failure prediction model using the emerging technique of decision trees and tests it numerically. Results indicate that the proposed model predicts business failure better than other commonly used approaches such as discriminant analysis.

Research has documented the impact of combat trauma on psychological functioning but less is known about the measurement of positive changes after military deployments. This study examined the factor structure of the Posttraumatic Growth Inventory (PTGI; Tedeschi & Calhoun, 1996) on a sample of active duty soldiers (n = 3537) exposed to combat in Iraq or Afghanistan. Confirmatory factor analyses (CFA) were conducted to test a 5-factor model and a single higher-order factor model. CFA results indicated that both models fit the data equally well and provide support for using both the whole scale and a multidimensional scale. The use of the PTGI in military research and the limitations of the current study are discussed. Keywords: Posttraumatic growth; confirmatory factor analysis; post traumatic growth inventory; military; combat exposure DOI: 10.1002/jclp.20692

Problem statement: In facial biometrics, face features are used as the required human traits for automatic recognition. Feature extracted from face images are significant for face biometrics system performance. Approach: In this thesis, a framework of facial biometric was designed based on two subspace methods i.e., Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). First, PCA is used for dimension reduction, where original face images are projected into lower-dimensional face representations. Second, LDA was proposed to provide a solution of better discriminant. Both PCA and LDA features were presented to Euclidean distance measurement which is conveniently used as a benchmark. The algorithms were evaluated in face identification and verification using a standard face database-AT and T and a locally collected database-CBE. Each database consists of 400 images and 320 images respectively. Results: LDA-based methods outperform PCA for both face identification and verification. For face identification, PCA achieves accuracy of 91.9% (AT and T) and 76.7% (CBE) while LDA 94.2% (AT and T) and 83.1% (CBE). For face verification, PCA achieves Equal Error Rate (EER) of 1.15% (AT and T), 7.3% (CBE) while LDA 0.78% (AT and T) and 5.81% (CBE). Conclusion/Recommendations: This study had proved that, when given sufficient training samples, LDA is able to provide better discriminant ability in feature extraction for face biometrics. Key words: Face biometrics, identification, verification, principal component analysis, linear discriminant analysis, equal error rate, INTRODUCTION Biometric is the technology of measuring intrinsic physical and behavior traits of human for authentication purpose. Emerging of this technology is motivated by traditional methods limitations in identity verification. [...]

This research study aims to identify strategic assets which currently drive and enhance the organizational capabilities of construction firms. There were 258 sets of questionnaires assessing the level of importance given to 106 substantial resources underlying six organizational capabilities of Thai construction firms that were analyzed. Using factor analysis, these 106 items were reduced to 14, which were termed strategic assets. These 14 strategic assets were then classified based on their influence on the six organizational capabilities. The results indicate that Thai construction firms concentrate mostly on developing excellent reputation, creating strong bargaining power with suppliers and subcontractors, and strengthening the firm's financial stability. However, they do not give much importance to effective risk and investment management, continuous development and innovation, and explicit strategic management. These findings provide in-depth insight to comprehensively understanding a Thai construction firm's capabilities. These 14 strategic assets should thereafter be used to develop a practical tool for managers of construction firms to evaluate their firm's strengths and weaknesses as well as to identify strategic assets required to enhance competitiveness in the market. DOI: 10.1061/(ASCE)CO.1943-7862.0000091 CE Database subject headings: Assets; Construction companies; Organizations; Thailand; Business management.

The article studies the suitability of smooth support vector machines (SSVM) for predicting the default risk of companies using variable selection and over sampling. Findings suggest that how different models could be used jointly to support the decision-making process of loan officers.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.forsciint.2009.04.012 Byline: Paula N. Gonzalez, Valeria Bernal, S. Ivan Perez Keywords: Sexual dimorphism; Sciatic notch; Ischiopubic complex; Semilandmarks; Discriminant analysis; k-Means clustering Abstract: Sex estimation of skeletal remains is an important issue in both forensics and bioarchaeology. The chance of attaining a high level of accuracy regarding sex allocations is related to the skeletal component analyzed and the ability of the techniques employed to describe shape and size differences among the sexes. Current opinion regards the hip bone as the most reliable sex indicator because it is the most dimorphic bone, particularly in adult individuals. The aim of this study was therefore to analyze the greater sciatic notch and the ischiopubic complex morphology by employing geometric morphometric techniques, based on semilandmark and multivariate statistical methods, in order to develop a reliable and accurate technique for adult sex estimation. The sample analyzed consisted of 121 adult left hip bones randomly selected from the collection of documented skeletons housed at the Museu Antropologico de Coimbra. Morphometric analysis was based on coordinates of landmarks and semilandmarks of the ilium and ischiopubic regions that were digitized on 2D photographic images. Discriminant analysis with leave-one-out cross-validation and k-means clustering of shape and shape-size variables were used in order to classify individuals by sex. For the greater sciatic notch, average accuracy of 90.9% was achieved with both multivariate analyses based on shape variables. For the ischiopubic complex, the values obtained with shape variables were 93.4% and 90.1% for discriminant and k-means, respectively. Females were misclassified more frequently than males, especially for the ischiopubic complex. When multivariate statistical analyses were performed using shape-size variables, the percentages of correct classifications were lower than those obtained with shape variables. We conclude that the use of geometric morphometrics and multivariate statistics is a reliable method to quantify pelvic shape differences between the sexes and could be applied to discriminate between females and males. Author Affiliation: Division Antropologia, Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, Paseo del Bosque s/n, La Plata 1900, Argentina Article History: Received 13 August 2008; Revised 30 March 2009; Accepted 14 April 2009

Parametric methods of classification assume specific parametric models for competing population densities (e.g., Gaussian population densities can lead to linear and quadratic discriminant analysis) and they work well when these model assumptions are valid. Violation in one or more of these parametric model assumptions often leads to a poor classifier. On the other hand, nonparametric classifiers (e.g., nearest-neighbor and kernel-based classifiers) are more flexible and free from parametric model assumptions. But, the statistical instability of these classifiers may lead to poor performance when we have small numbers of training sample observations. Nonparametric methods, however, do not use any parametric structure of population densities. Therefore, even when one has some additional information about population densities, that important information is not used to modify the nonparametric classification rule. This paper makes an attempt to overcome these limitations of parametric and nonparametric approaches and combines their strengths to develop some hybrid classification methods. We use some simulated examples and benchmark data sets to examine the performance of these hybrid discriminant analysis tools. Asymptotic results on their misclassification rates have been derived under appropriate regularity conditions. Index Terms--Bayes risk, bandwidth, kernel density estimation, LDA, misclassification rate, multiscale smoothing, nearest neighbor, QDA.

In the Mediterranean, bottom trawlers are multispecific and frequently apply different fishing tactics (FTs) even during the same fishing trip. Up to four individual FTs were distinguished in the study area where fishermen usually use mixtures of different FTs in daily fishing trips. Identifying the FTs actually performed is a key issue in traditional stock assessment methods. In this paper, we compare the performance of discriminant analysis and artificial neural networks for predicting FTs from the species composition of daily sale bills. We used data on the landings of each vessel from daily sale bills along with information on the FT actually performed, which was obtained by onboard observers who interviewed skippers about the FTs that they planned to employ. Discriminant analysis and artificial neural networks achieved comparable overall results and the success of predictions depended on both the sample size of the different data subsets (balancing) and the similarity between the species composition of different FTs (overlapping). Although the percentage of correct predictions was high for FTs with more than 25 cases, success decreased when the sample sizes were small. In addition, success in predicting mixtures of two different FTs increased with increasing dissimilarity between their corresponding species compositions. Dans la Mediterranee, les chalutiers de fond sont multispecifiques et ils utilisent frequemment differentes tactiques de peche (FTs) meme durant une seule sortie en mer. Nous avons identifie jusqu'a quatre FTs differentes dans la zone d'etude ou les pecheurs utilisent une combinaison de FTs differentes dans leurs sorties journalieres de peche. L'identification des FTs effectivement utilisees est une donnee essentielle pour les methodes traditionnelles d'evaluation des stocks. Dans notre etude, nous comparons la performance de l'analyse discriminante et des reseaux de neurones artificiels pour predire les FTs a partir des compositions d'especes des factures quotidiennes de vente. Nous utilisons des donnees sur les debarquements de chaque navire tirees des factures de vente, ainsi que des informations sur les FTs effectivement utilisees obtenues par des observateurs a bord qui ont interroge les capitaines sur les FTs qu'ils comptaient utiliser. L'analyse discriminante et les reseaux de neurones artificiels fournissent globalement des resultats comparables et le succes des predictions depend a la fois de la taille de 1'echantillon des differents sous-ensembles de donnees (equilibrage) et de la similarity de la composition specifique des differentes FTs (chevauchement). Bien que le pourcentage de predictions justes soit eleve pour les FTs representees par plus de 25 cas, le succes decroit lorsque la taille des echantillons est petite. De plus, le succes de la prediction pour des melanges de deux FTs differentes augmente en fonction directe de la dissemblance entre leurs compositions specifiques respectives. [Traduit par la Redaction], Introduction The Mediterranean demersal fisheries are both multifleet and multispecific, with more than 100 species in their landings (Moranta et al. 2008). In addition, both small-scale and bottom-trawl fisheries are [...]

The fault diagnosis of Gas Insulated Switchgear (GIS) partial discharge (PD) is significant for mastering the essence of defects within the GIS accurately and guiding its maintenance. This paper designed four typical single defects and three mixed defects in GIS, then PD 3-dimensional patterns were constructed based on mass sample gathered by the very-high frequency and high speeds sampling systems. As to the problems in linear Fisher analysis used for diagnosing the failures of PD, this paper puts forward kernel statistical uncorrelated optimum discriminant vectors (KSUODV) algorithm to solve the problem of non-linear feature extraction in High-dimensional feature space based on kernel method and to eliminate statistical correlation between transformed sample features. The recognition result of the PD patterns of seven defects obtained in the lab proves that KSUODV algorithm for recognition is prior to SUODV algorithm. Index Terms--Gas insulated switchgear (GIS), partial discharge (PD), Pattern recognition, Kernel, Fisher discriminant analysis.

Byline: R. D. Wiggins (1), G. Netuveli (2), M. Hyde (3), P. Higgs (4), D. Blane (2) Keywords: Quality of life; Confirmatory and exploratory factor analysis; Shortened 12-item version of CASP-19; Ageing Abstract: This paper describes the conceptual development of a self-enumerated scale of quality of life (CASP-19) and presents an empirical evaluation of its structure using a combination of exploratory and confirmatory factor analytic approaches across three different survey settings for older people living in England and Wales in the new millennium. All evaluations are conducted using MPlus which allows the analyst to evaluate the properties of the scale for a set of multivariate categorical items which are subject to item non-response. CASP-19 is a subjective measure of well-being derived from an explicit theory of human need spanning four life domains: control, autonomy, self-realisation and pleasure. Put formally, CASP-19 is a self-reported summative index consisting of 19 Likert scale items. The three survey settings include a postal survey of 263 people in early old age followed up from childhood when the respondents were first interviewed in the 1930's, the first wave (2002) of the English Longitudinal Study of Ageing (ELSA_1) and the eleventh wave of the British Household Panel Survey (BHPS_11) also conducted in 2002. These nationally representative surveys consisted of 9300 and 6471 respondents aged 55 years and older. The Boyd-Orr sample provides an exploratory context for the evaluation and ELSA_1 together with BHPS_11 provide the opportunity for confirmatory analyses of three measurement models. There is some support for the use of CASP-19 as a stand alone summative index. However, the analysis reveals that a shortened 12-item scale which combines the life domains 'control and autonomy' in a second order measurement model is the recommended model for analysts. The work was funded under the UK's Economic and Social Research Council's Growing Older Programme and their Priority Network on Human Capability and Resilience. Grant Nos. L480254016 & L326253061. Author Affiliation: (1) Department of Quantitative Social Science, The Faculty of Policy and Society, The Institute of Education, University of London, 20 Bedford Way, London, WC1H 0AL, UK (2) Imperial College of Science, Technology and Medicine, London, UK (3) Sheffield Hallam University, Sheffield, UK (4) University College, London, UK Article History: Registration Date: 12/11/2007 Received Date: 11/12/2006 Accepted Date: 10/11/2007 Online Date: 29/12/2007

The article explains a recently developed MM (majorization-minimization) algorithm, which is employed for studying the multicategory linear discrimination observed among the vertices of a regular simplex in Euclidean space. The algorithm is shown to be extremely advantageous, as it is based on the quadratic majorization and is highly accurate and fast.

We introduce a new state discrimination problem in which we are given additional information about the state after the measurement, or more generally, after a quantum memory bound applies. The following special case plays an important role in quantum cryptographic protocols in the bounded storage model: Given a string x encoded in an unknown basis chosen from a set of mutually unbiased bases (MUBs), you may perform any measurement, but then store at most q qubits of quantum information, and an unlimited amount of classical information. Later on, you learn which basis was used. How well can you compute a function f(x) of x, given the initial measurement outcome, the q qubits, and the additional basis information? We first show a lower bound on the success probability for any balanced function, and any number of mutually unbiased bases, beating the naive strategy of simply guessing the basis. We then show that for two bases, any Boolean function f(x) can be computed perfectly if you are allowed to store just a single qubit, independent of the number of possible input strings x. However, we show how to construct three bases, such that you need to store all qubits in order to compute f(x) perfectly. We then investigate how much advantage the additional basis information can give for a Boolean function. To this end, we prove optimal bounds for the success probability for the AND and the XOR function for up to three mutually unbiased bases. Our result shows that the gap in success probability can be maximal: without the basis information, you can never do better than guessing the basis, but with this information, you can compute f(x) perfectly. We also give an example where the extra information does not give any advantage at all. Index Terms--Bounded quantum storage, quantum cryptography, state discrimination.

We used confirmatory factor analysis (CFA) to investigate the factor structure of several models of Arnold et al.'s Parenting Scale [Arnold et al. (1993). Psychological Assessment, 5, 137-144] across children from various age groups and races. Participants were parents of children (ages 2-16 years) presenting to four community-based pediatric practices for routine care. Parents completed questionnaires pertaining to various aspects of parenting and child behavior problems. Results indicated that a two-factor revision proposed by Reitman et al. [(2001). Journal of Clinical Child Psychology, 30, 514-524] represented the data well and better than the original three-factor structure and other two-factor models. Results from multigroup CFA analyses indicated that this factor structure did not vary across child sex, child age, and parental race. Results of validity analyses indicated that scores on both factors were related to reports of children's behavior and parental affect and cognitions. This study was the first to use multigroup CFA procedures to demonstrate that relationships between individual items and factors of the Parenting Scale are similar for parents of children across various age groups. Keywords Parenting * Assessment Behavior problems * Overreactivity * Laxness

Many situations arise when it is desired to recover from a noisy mixture of data both an estimate of a propagating source signal as well as its angular position. Accordingly, the problem of source separation and tracking for time-varying systems of moving sources is studied. Using techniques borrowed from independent component analysis (ICA) and numerical linear algebra, a processing solution is formulated. The ULV decomposition (ULVD), a member of the complete orthogonal decompositions (CODs) family is used to normalize and decorrelate the data. Since the ULVD is known to stably update and downdate with time-varying data, this step is named ULVD adaptive whitening. The ICA-based EASI (equivariant adaptive separation based on independence) algorithm is adapted to provide not only separated sources but direction-of-arrival (DOA) estimates. Developed improvements to this technique are an adaptive filter method for estimating the mixing matrix from the newly estimated signals as well as a simple cross-over detection scheme. Finally, the developed solutions ate tested in simulation examples using meaningful performance measures.

A study was conducted to establish the efficacy of four-factor obsessive-compulsive disorder (OCD) symptom structure for use in child, adolescent and adult groups. Results indicated that the four-factor OCD structure is inadequate for use in children, adolescent and adult age groups.

Unanticipated market conditions as well as project-related risks can easily lead to cost overruns in international construction projects. For a contractor to be financially successful in international projects, a careful examination of the project is a prerequisite to understanding the cost variance characteristics. Based on the reasonably accurate characterization of the cost performance, the markup or contingency amount is determined to ensure both a decent level of profit and a good chance of winning the contract. This paper presents a classification model to categorize international construction projects, particularly faced by Korean contractors, into five cost-variation classes: extreme cost overrun, moderate cost overrun, neutral, moderate cost saving, and extreme cost saving. The model is able to characterize an international project for its cost performance prediction in comparison to the contractor's initial cost estimate. A linear discriminant analysis is utilized to develop the predictive classification model with the support of the bootstrap method. Tests show that the proposed model is able to help cost estimators determine a proper level of cost contingency before bidding on an international project. CE Database subject headings: Cost control: Forecasting; Foreign projects; Risk management; Project management.

Model selection of factor analysis (FA) model in radar high-resolution range profile (HRRP) statistical recognition is discussed. Results suggest an improvement in recognition performance with higher recognition efficiency.

We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution of the model is an unsupervised generalization of linear discriminant analysis. This provides a completely new approach to one of the most established and widely used classification algorithms. The performance of the model is then demonstrated on a number of real and artificial data sets. Index Terms--Dimensionality reduction, clustering, discriminant analysis, probabilistic algorithms.

This work proposes a subspace approach that regularizes and extracts eigenfeatures from the face image. Eigenspace of the within-class scatter matrix is decomposed into three subspaces: a reliable subspace spanned mainly by the facial variation, an unstable subspace due to noise and finite number of training samples, and a null subspace. Eigenfeatures are regularized differently in these three subspaces based on an eigenspectrum model to alleviate problems of instability, overfitting, or poor generalization. This also enables the discriminant evaluation performed in the whole space. Feature extraction or dimensionality reduction occurs only at the final stage after the discriminant assessment. These efforts facilitate a discriminative and a stable low-dimensional feature representation of the face image. Experiments comparing the proposed approach with some other popular subspace methods on the FERET, ORL, AR, and GT databases show that our method consistently outperforms others. Index Terms--Face recognition, linear discriminant analysis, regularization, feature extraction, subspace methods.

Linear discriminant analysis was used to determine the spatial distribution and economic growth of Thailand's furniture industry. Analysis and dynamics of economic development are discussed in relation to globalization of labor-intensive industries.

Compared with published norms, African Americans endorse significantly more items intended to assess pathological anxiety about contamination on self-report instruments for obsessive-compulsive disorder. The current study suggests that this is not due to greater psychopathology in African Americans, but rather to differences in normal attitudes about cleanliness that also influence responses to items intended to assess anxiety pathology. Contamination items from obsessive-compulsive disorder (OCD) scales including the Padua Inventory [Sanavio E. (1988). Obsessions and compulsions: The Padua Inventory. Behaviour Research and Therapy, 26(2), 169-177] were supplemented with cleanliness attitude items and administered to Black and White participants (N = 1483). An exploratory factor analysis suggested a three-factor solution: one factor that encompassed pathological anxiety, and two that expressed attitudes about cleanliness, grooming, and domestic animals. African Americans scored significantly higher on all three factors. A confirmatory factor analysis demonstrated that the difference between Black and White participants on the pathological anxiety factor was eliminated when differences on the attitude factors were controlled statistically. Keywords: Factor analysis; Assessment: Obsessive-compulsive disorder; Ethnic differences; Contamination; Anxiety: Attitudes

Knowledge about soil solution chemicals is important for assessing their mobility, availability, migration to groundwater and toxicity to plants. The objective of this study was to apply factor analysis to data obtained on soil solution chemicals during a one-year monitoring program in a controlled experiment with tannery effluents disposed on the soil surface, to extract information on their relationship and identify the main contaminants. Seventeen chemical parameters were monitored at six different depths on soil profile, focusing on metals and nitrate in soil solution. Four Factors accounted for 79.20% of the total variance, of which the most important were: Factor 1 (48.35%) showed significant loadings for [Mn.sup.2+], [Na.sup.+], [K.sup.+], [Ca.sup.2+], [Mg.sup.2+], [Cl.sup.-], [Pb.sup.2+] and electric conductivity, strongly influenced by high load effluent disposal; Factor 2 (12.21%) was related with [SO.sub.4.sup.2+], Factor 3 (10.16%) associated with [Cu.sup.2+] and [Zn.sup.2+] and Factor 4 (8.49%) associated with nitrogen mineralization dynamics after high disposal. Key words: Soil solution, groundwater, tannery effluents, factor analysis, INTRODUCTION Industrial activities are capable of generating soil and groundwater pollution as a result of the emission of liquid effluents or waste disposal practices(1-5). The tanning industry is considered an [...]

ABSTRACT. Classification of high-latitude landscapes into their appropriate biomes is important for many climate and global change-related issues. Unfortunately, large-scale, high-spatial-resolution observations of plant assemblages associated with these regions are [...]

The article describes the distinctively normalized joint diagonalization (DNJD) algorithm, a new approach to joint diagonalization for a set of symmetric matrices with a general matrix. The approach is simple, effective and computationally efficient in the context of blind source separation (BSS).

This paper presents a unified criterion, Fisher + kernel criterion (FKC), for feature extraction and recognition. This new criterion is intended to extract the most discriminant features in different nonlinear spaces, and then, fuse these features under a unified measurement. Thus, FKC can simultaneously achieve nonlinear discriminant analysis and kernel selection. In addition, we present an efficient algorithm Fisher + kernel analysis (FKA), which utilizes the bilinear analysis, to optimize the new criterion. This FKA algorithm can alleviate the ill-posed problem existed in traditional kernel discriminant analysis (KDA), and usually, has no singularity problem. The effectiveness of our proposed algorithm is validated by a series of face-recognition experiments on several different databases. Index Terms--Bilinear analysis, discriminant analysis, face recognition, feature extraction, Fisher criterion, kernel selection.

This study represents an effort to better understand the latent structure of anxiety sensitivity (AS), a well established affect-sensitivity individual difference factor, among youth by employing taxometric and factor analytic approaches in an integrative manner. Taxometric analyses indicated that AS, as indexed by the Child Anxiety Sensitivity Index (CASI; Silverman, Flesig, Rabian, & Peterson, 1991), demonstrates taxonic latent class structure in a large sample of youth from North America (N=4,462; [M.sub.age]=15.6 years; SD=1.3). Subsequent confirmatory factor analyses indicated that the latent continuous, multidimensional, 4-factor model of AS among youth (Silverman, Goedhart, Barrett, & Turner, 2003) provided good fit for the CASI data among the complement class ('normative form' of AS), but not among the taxon class ('high-risk form' of AS). EFAs supported the prediction that the AS taxon demonstrates a unique, heretofore unexplored latent continuous, unidimensional factor structure among youth. Findings are discussed in relation to refining our understanding of the

Discussed is the development and psychometric analysis of a measure of rape-supportive attitudes and beliefs called the Rape Attitudes and Beliefs Scale (RABS), intended for the use with college men. Items were developed from a literature review of 'rape myths' that were correlated to some measure of sexual aggression. An exploratory factor analysis using only male participants revealed five factors: a) justifications for sexual aggression based on women's behavior, b) belief that women should hold more responsibility for sexual assault, peer c) pressure/need for sexual status and misreading women's sexual intent, d) acceptance of the use of alcohol and coercive tactics to acquire sexual compliance, and e) dislike for the feminine and acceptance of traditional gender roles. Initial reliability and validity studies were favorable for the RABS, including evidence that these factors were positively related to measures of sexual aggression. Each factor demonstrated differential power to predict sexual aggression, with justifications being the most powerful. Implications for counseling and education are discussed. Keywords: assessment, assessment instruments; attitudes: beliefs: college men: college/university; factor analysis; men's studies; rape myths: sexual aggression