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Myrtle rust, caused by the fungus Austmpuccinia psidii, is a serious disease, which affects many Myrtaceae species. Commercial nurseries that propagate Myrtaceae species are prone to myrtle rust and require a reliable method that allows previsual and early detection of the disease. This study uses time-series thermal imagery and visible-to-short-infrared spectroscopy measurements acquired over 10 days from 81 rose apple plants (Syzygium jumbos') that were either inoculated with myrtle rust or maintained disease-free. Using these data, the objectives were to (i) quantify the accuracy of models using thermal indices and narrowband hyperspectral indices (NBHI) for previsual and early detection of myrtle rust using data from older resistant green leaves and young susceptible red leaves and (ii) identify the most important NBHI and thermal indices for disease detection. Using predictions made on a validation dataset, models using indices derived from thermal imagery were able to perfectly (Fl score = 1.0; accuracy = 100%) distinguish control from infected plants previsually one day before symptoms appeared (1 DBS) and for all stages after early symptoms appeared. Compared with control plants, plants with myrtle rust had lower and more variable normalized canopy temperature, which was associated with higher stomatai conductance and transpiration. Using NBHI derived from green leaves, excellent previsual classification was achieved 3 DBS, 2 DBS, and 1 DBS (Fl score range = 0.89 to 0.94). The accurate characterization of myrtle rust during previsual and early stages of disease development suggests that a robust detection methodology could be developed within a nursery setting. [ABSTRACT FROM AUTHOR]

Subjective effort can significantly affect the ability of humans to act optimally in dynamic manipulation tasks. In a previous study, we designed a complex object coupling manipulation task that required tight performance and induced high cognitive workload. We hypothesize that strong-effort-related physiological reactivity during the dynamic manipulation task improves the user performance in an undesired task feedback situation. To test this hypothesis, using the motor intentions' discrimination from electroencephalogram (EEG) measurements, we evaluate the effort expended by 20 participants in a controlling task with constraints involving complex coupling objects. Specifically, the finer motor decisions are obtained from the controlling information in EEG by using two fingers from the same hand rather than two hands. The motor intention is decoded from a task-dependent EEG through a regularized discriminant analysis, and the area under the curve is ∼ 0. 9 4. Furthermore, we compare the undesired and desired task feedback conditions along with the individual's effort dynamic adjustment, and investigate whether the undesired task feedback improved the discrimination of the motor activities. A stronger effort to attain the desired feedback state corresponds to improved motor activity discrimination from the EEG in the undesired task feedback scenario. The differences in the brain activities under the undesired and desired task feedback conditions are analyzed using brain-network-based topographical scalp maps. Our experiment provides preliminary evidence that inducing strong effort can improve discrimination performance during highly demanding tasks. This finding can advance our understanding of human attention, potentially improve the accuracy of intention recognition, and may inspire better EEG acquisition contexts. [ABSTRACT FROM AUTHOR]

Functional near-infrared spectroscopy (fNIRS) is an emerging technique in studying cerebral hemodynamics; however, consensus on the analysis methods and the clinical applications has yet to be established. In this study, we demonstrate the results of a pilot fNIRS study of cerebral hemodynamic response (HR) evoked by pneumotactile and sensorimotor stimuli on the dominant hand. Our goal is to find the optimal stimulus parameters to maximally evoke HR in the primary somatosensory and motor cortices. We use a pulsatile pneumatic array of 14 tactile cells that were attached to the glabrous surface of the dominant hand, with a patterned stimulus that resembles saltation at three distinct traverse velocities [10, 25, and 45 cm/s]. NIRS optodes (16 sources; 20 detectors) are bilaterally and symmetrically placed over the pre-and post-central gyri (M1 and S1). Our objective is to identify the extent to which cerebral HR can encode the velocity of the somatosensory and/or motor stimuli. We use common spatial pattern for feature extraction and regularized-discriminant analysis for classifying the fNIRS time series into velocity classes. The classification results demonstrate discriminatory features of the fNIRS signal from each distinct stimulus velocity. The results are inconclusive regarding the velocity which evokes the highest intensity of hemodynamic response. [ABSTRACT FROM AUTHOR]

In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to develop strategies that will achieve better gait recognition results than those achieved by classical classification methods. In our approach, particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) are used. These techniques tune the observation weights and hyperparameters of the RDA method to minimize the objective function. The experiments conducted on the GPJATK dataset proved the validity of the proposed concept.

We identify canopy species in a Hawaiian tropical forest using supervised classification applied to airborne hyperspectral imagery acquired with the Carnegie Airborne Observatory-Alpha system. Nonparametric methods (linear and radial basis function support vector machine, artificial neural network, and k-nearest neighbor) and parametric methods (linear, quadratic, and regularized discriminant analysis) are compared for a range of species richness values and training sample sizes. We find a clear advantage in using regularized discriminant analysis, linear discriminant analysis, and support vector machines. No unique optimal classifier was found for all conditions tested, but we highlight the possibility of improving support vector machine classification with a better optimization of its free parameters. We also confirm that a combination of spectral and spatial information increases accuracy of species classification: we combine segmentation and species classification from regularized discriminant analysis to produce a map of the 17 discriminated species. Finally, we compare different methods to assess spectral separability and find a better ability of Bhattacharyya distance to assess separability within and among species. The results indicate that species mapping is tractable in tropical forests when using high-fidelity imaging spectroscopy. [ABSTRACT FROM AUTHOR]

Given a classifier, we describe a general method to construct a simple linear classification rule. This rule, called the tangent classifier, is obtained by computing the tangent hyperplane to the separation boundary of the groups (generated by the initial classifier) at a certain point. When applied to a quadratic region, the tangent classifier has a neat closed-form expression. We discuss various examples and the application of this new linear classifier in two situations under which standard rules may fail: when there is a fraction of outliers in the training sample and when the dimension of the data is large in comparison with the sample size. [ABSTRACT FROM AUTHOR]

Abstract: Conventionally, chemical patterns of gunshot residues (GSR) can be used for identification of a suspect involved in criminal fire arm incidents. Furthermore, metals composition in GSR is well related with the ammunition brand. In Chile the two main ammunition brands used are FAMAE and CBC. Metals, such as Pb, Ba, Sb, Cu, Zn and Ca are common elements detected in both brands. This work describes the application of both conventional and chemometric analysis of data (Regularized Discriminant Analysis, RDA) for differentiation of ammunition brands according to the metal patterns of GSR collected from shooter individuals. Real samples of GSR were collected from hands (dorsal region) of both shooters and non-shooters. Metals were analyzed using the techniques Atomic Absorption Spectrometry (AAS) and Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-OES). By means of conventional plotting techniques for differentiation, such as binary and ternary plots, differences between the two brands are observed although without quantitative certainty. For the first time, applying chemometric analysis, such as regularized discriminant analysis (RDA), the investigated ammunition brands can be classified and differentiated correctly with 100% certainty. [Copyright &y& Elsevier]

Abstract: Dimensionality reduction has proved to be a beneficial tool in learning problems. Two of the main advantages provided by dimensionality reduction are interpretation and generalization. Typically, dimensionality reduction is addressed in two separate ways: variable selection and feature extraction. However, in the recent years there has been a growing interest in developing combined schemes such as feature extraction with built-in feature selection. In this paper, we look at dimensionality reduction as a rank-deficient problem that embraces variable selection and feature extraction, simultaneously. From our analysis, we derive a weighting algorithm that is able to select and linearly transform variables by fixing the dimensionality of the space where a relevance criterion is evaluated. This step enforces sparseness on the resulting weights. Our main goal is dimensionality reduction for classification problems. Namely, we introduce modified versions of principal component analysis (PCA) by expectation maximization (EM) and linear regularized discriminant analysis (RDA). Finally, we propose a simple extension of WRDA that deals with functional features. In this case, observations are described by a set of functions defined over the same domain. Methods were put to test on artificial and real data sets showing high levels of generalization even for small sized training samples. [Copyright &y& Elsevier]

Abstract: In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for k-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, discriminant analysis via support vectors (SVDA), is proposed. First, the SVM is employed to compute an optimal direction to discriminant each two classes. Then, the criteria of class separability is constructed. At last, the projection matrix is computed. The kernel mapping idea is used to derive the non-linear version, kernel discriminant via support vectors (SVKD). In SVDA, only support vectors are involved to compute the transformation matrix. Thus, the computational complexity can be greatly reduced for kernel based feature extraction. Experiments carried out on several standard databases show a clear improvement on LDA-based recognition. [Copyright &y& Elsevier]

The evolutionary history of aquatic invasion in birds would be incomplete without incorporation of extinct species. We show that aquatic affinities in fossil birds can be inferred by multivariate analysis of skeletal features and locomotion of 245 species of extant birds. Regularized discriminant analyses revealed that measurements of appendicular skeletons successfully separated diving birds from surface swimmers and flyers, while also discriminating among different underwater modes of swimming. The high accuracy of this method allows detection of skeletal characteristics that are indicative of aquatic locomotion and inference of such locomotion in bird species with insufficient behavioural information. Statistical predictions based on the analyses confirm qualitative assessments for both foot-propelled (Hesperornithiformes) and wing-propelled ( Copepteryx) underwater locomotion in fossil birds. This is the first quantitative inference of underwater modes of swimming in fossil birds, enabling future studies of locomotion in extinct birds and evolutionary transitions among locomotor modes in avian lineage. [ABSTRACT FROM AUTHOR]

We present a simple new criterion for classification, based on principles from lossy data compression. The criterion assigns a test sample to the class that uses the minimum number of additional bits to code the test sample, subject to an allowable distortion. We demonstrate the asymptotic optimality of this criterion for Gaussian distributions and analyze its relationships to classical classifiers. The theoretical results clarify the connections between our approach and popular classifiers such as maximum a posteriori (MAP), regularized discriminant analysis (RDA), k-nearest neighbor (k-NN), and support vector machine (SVM), as well as unsupervised methods based on lossy coding. Our formulation induces several good effects on the resulting classifier. First, minimizing the lossy coding length induces a regularization effect which stabilizes the (implicit) density estimate in a small sample setting. Second, compression provides a uniform means of handling classes of varying dimension. The new criterion and its kernel and local versions perform competitively on synthetic examples, as well as on real imagery data such as handwritten digits and face images. On these problems, the performance of our simple classifier approaches the best reported results, without using domain-specific information. All MATLAB code and classification results are publicly available for peer evaluation at http://perception.csl.uiuc.edu/coding/home.htm. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we seek a Gaussian mixture model (GMM) of the class-conditional densities for plug-in Bayes classification. We propose a method for setting the number of the components and the covariance matrices of the class-conditional GMMs. It compromises between simplicity of the model selection based on the Bayesian information criterion (BIC) and the high accuracy of the model selection based on the cross-validation (CV) estimate of the correct classification rate. We apply an idea of Friedman [Friedman, J.H. 1989. Regularized discriminant analysis. J. Amer. Statist. Assoc., 84, 165–175] to shrink a predefined covariance matrix to a parameterization with substantially reduced degrees of freedom (reduced number of the adjustable parameters). Our method differs from the original Friedman’s method by the meaning of the shrinkage. We operate on matrices computed for a certain class while the Friedman’s method shrinks matrices from different classes. We compare our method with the conventional methods for setting the GMMs based on the BIC and CV. The experimental results show that our method has the potential to produce parameterizations of the covariance matrices of the GMMs which are better than the parameterizations used in other methods. We observed significant enlargement of the correct classification rates for our method with respect to the other methods which is more pronounced as the training sample size decreases. The latter implies that our method could be an attractive choice for applications based on a small number of training observations. [Copyright &y& Elsevier]

Abstract: A gunshot residue sample that was collected from an object or a suspected person is automatically searched for gunshot residue relevant particles. Particle data (such as size, morphology, position on the sample for manual relocation, etc.) as well as the corresponding X-ray spectra and images are stored. According to these data, particles are classified by the analysis-software into different groups: ‘gunshot residue characteristic’, ‘consistent with gunshot residue’ and environmental particles, respectively. Potential gunshot residue particles are manually checked and – if necessary – confirmed by the operating forensic scientist. As there are continuing developments on the ammunition market worldwide, it becomes more and more difficult to assign a detected particle to a particular ammunition brand. As well, the differentiation towards environmental particles similar to gunshot residue is getting more complex. To keep external conditions unchanged, gunshot residue particles were collected using a specially designed shooting device for the test shots revealing defined shooting distances between the weapon''s muzzle and the target. The data obtained as X-ray spectra of a number of particles (3000 per ammunition brand) were reduced by Fast Fourier Transformation and subjected to a chemometric evaluation by means of regularized discriminant analysis. In addition to the scanning electron microscopy in combination with energy dispersive X-ray microanalysis results, isotope ratio measurements based on inductively coupled plasma analysis with mass-spectrometric detection were carried out to provide a supplementary feature for an even lower risk of misclassification. [Copyright &y& Elsevier]

Linear and quadratic discriminant analysis are likely to lead to unstable models and poor predictions in the presence of quasicolinearity among variables or in the case of the small sample and high-dimensional setting. A simple regularization procedure is proposed to cope with this problem. It is based on the introduction of a tuning parameter that draws a line between linear or quadratic discriminant analysis that is based on Mahalanobis distance and discriminant analysis based on the identity matrix. The tuning parameter is customized to individual situations by minimizing the cross-validated misclassification risk. The efficiency of the method of analysis in comparison with existing procedures is demonstrated on the basis of a data set and a large simulation study. [ABSTRACT FROM AUTHOR]

Investigations of natural and engineered environmental systems require achieving a complete characterization and identification of contaminants of concern. However, the differentiation of lipid molecular markers (MMs) that originated from various sources is difficult when based simply on their chemical compositions. A comprehensive fractionation protocol of lipid MMs is thus needed for discriminating contamination sources using chemometric (i.e., mathematical and statistical) techniques. The lack of information about the environmental quality of the coastal environment of Alexandria (Egypt) and the impact of anthropogenic sources of pollution in the area have initiated research to study the different extractable lipid classes and MMs in both natural and engineered environmental systems. Samples representing the complexity of the analyzed Alexandria environment were analyzed qualitatively and quantitatively for their lipid classes. A chemometric approach for the interpretation of the lipid data is presented. This unique approach consists of analysis of variance (ANOVA), discriminant partial least squares (D-PLS), and regularized discriminant analysis (RDA). ANOVA was used to compare the relative magnitudes of sample site and type variances. A variable selection approach based on the PLS regression coefficients was proposed to identify the most important lipid classes with their MMs for the classification, and to improve the results. RDA used a regularized covariance matrix estimate for the conventional statistical discriminant analysis methods. The results in this analysis indicated that: (a) the D-PLS and RDA methods provide satisfactory classification of lipid classes, with RDA being slightly better, and (b) the variable selection strategy was able to improve the classification results, and to help identify the most important lipid classes and contaminant MMs for distinguishing and characterizing the environmental samples. [ABSTRACT FROM AUTHOR]

This paper addresses two problems in linear discriminant analysis (LDA) of face recognition. The first one is the problem of recognition of human faces under pose and illumination variations. It is well known that the distribution of face images with different pose, illumination, and face expression is complex and nonlinear. The traditional linear methods, such as LDA, will not give a satisfactory performance. The second problem is the small sample size (S3) problem. This problem occurs when the number of training samples is smaller than the dimensionality of feature vector. In turn, the within-class scatter matrix will become singular. To overcome these limitations, this paper proposes a new kernel machine-based one-parameter regularized Fisher discriminant (K1PRFD) technique. K1PRFD is developed based on our previously developed one-parameter regularized discriminant analysis method and the well-known kernel approach. Therefore, K1PRFD consists of two parameters, namely the regularization parameter and kernel parameter. This paper further proposes a new method to determine the optimal kernel parameter in RBF kernel and regularized parameter in within-class scatter matrix simultaneously based on the conjugate gradient method. Three databases, namely FERET, Yale Group B, and CMU PIE, are selected for evaluation. The results are encouraging. Comparing with the existing LDA-based methods, the proposed method gives superior results. [ABSTRACT FROM AUTHOR]

Much work in discriminant analysis and statistical pattern recognition has been performed in the former Soviet Union. However, most results derived by former Soviet Union researchers are unknown to statisticians and statistical pattern recognition researchers in the West. We attempt to give a succinct overview of important contributions by Soviet Block researchers to several topics in the discriminant analysis literature concerning the small training-sample size problem. We also include a partial review of corresponding work done in the West. [Copyright &y& Elsevier]

In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to develop strategies that will achieve better gait recognition results than those achieved by classical classification methods. In our approach, particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) are used. These techniques tune the observation weights and hyperparameters of the RDA method to minimize the objective function. The experiments conducted on the GPJATK dataset proved the validity of the proposed concept. [ABSTRACT FROM AUTHOR]

• A new EEG evoked paradigm combining physical interface and virtual feedback under the dynamic coupling system operation task is proposed, the experimental paradigm can better simulate real-life dynamic scenes, and EEG signals can be better expressed. • By constructing the brain wave of complex tasks by calculating the phase fluctuations of the EEG, this can more intuitively understand the relationship between the potential neuron mechanism and brain consciousness when performing tasks. • Decoding the network features by parameter-optimized regularized discriminant analysis, a good decoding effect is obtained by this method. Most of the tasks involved in neurological rehabilitation are generally constrained. However the complexity of experimental paradigms with constraints for acquiring electroencephalography (EEG) are relatively low. In order to improve the level of arousal in the brain, we propose an energy-constrained dynamic and complex experimental paradigm. In this novel experimental paradigm, EEG signals collected under the novel paradigm have different motor intentions. In this study, we combine the phase synchronization method and common spatial pattern (CSP) to build functional brain networks of subjects. Based on this method, we investigate the correlation of the global and local features of the functional brain networks and use regularized discriminant analysis (RDA) to recognize features. The accuracy obtained by the proposed method for EEG intention recognition can reach 93.47% (p < 0.01). The results show that the proposed method can decode EEG intention with high recognition performance during the manipulation of complex objects, which lays a foundation for the study of neurological rehabilitation. [ABSTRACT FROM AUTHOR]