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This paper proposes an innovative spectral feature extraction (SFE) method called prototype space (PS) feature extraction (PSFE) based only on class spectra. The main novelties of the proposed SFE lie in the following: representing channels in a new space called PS, where they are characterized in terms of reflection properties of classes; and proposing uncertainty, angle, and distance measures to distinguish highly correlated and informative channels. Having clustered the channels by Fuzzy C-Means (FCM) in PS, highly correlated and isolated channels are separated by an uncertainty measure. Consequently, PSFE is built by a linear combination of spectra weighted by class membership values of channels that fall in each cluster. Furthermore, we will enrich PSFE with informative channels which are outlier channels identified through their angle and distance with respect to diagonal and cluster centers in PS. In contrast to the previous SFE methods, PSFE substitutes the search strategies with FCM clustering to find relevant channels. Moreover, instead of optimizing separability criteria, the accuracy of classification over a subset of training data is used to decide which disjoint optical region yields maximum accuracy. According to how class spectra are obtained, PSFE incorporates four approaches: knowledge based, supervised, semisupervised, and unsupervised. The latter three PSFE approaches are assessed in two main cases including with and without informative channels and compared with the conventional feature extraction methods. Experimental results demonstrated higher overall accuracy of PSFE compared to its conventional counterparts with limited sample sizes. Index Terms--Feature extraction (FE), hyperspectral images, remote-sensing image classification, spectral feature.

We propose an eigenvector-based heteroscedastic linear dimension reduction (LDR) technique for multiclass data. The technique is based on a heteroscedastic two-class technique which utilizes the so-called Chernoff criterion, and successfully extends the well-known linear discriminant analysis (LDA). The latter, which is based on the Fisher criterion, is incapable of dealing with heteroscedastic data in a proper way. For the two-class case, the between-class scatter is generalized so to capture differences in (co)variances. It is shown that the classical notion of between-class scatter can be associated with Euclidean distances between class means. From this viewpoint, the between-class scatter is generalized by employing the Chernoff distance measure, leading to our proposed heteroscedastic measure. Finally, using the results from the two-class case, a multiclass extension of the Chernoff criterion is proposed. This criterion combines separation information present in the class mean as well as the class covariance matrices. Extensive experiments and a comparison with similar dimension reduction techniques are presented. Index Terms--Linear dimension reduction, linear discriminant analysis, Fisher criterion, Chernoff distance, Chernoff criterion.

Many computer vision and pattern recognition problems may be posed as the analysis of a set of dissimilarities between objects. For many types of data, these dissimilarities are not euclidean (i.e., they do not represent the distances between points in a euclidean space), and therefore cannot be isometrically embedded in a euclidean space. Examples include shape-dissimilarities, graph distances and mesh geodesic distances. In this paper, we provide a means of embedding such non-euclidean data onto surfaces of constant curvature. We aim to embed the data on a space whose radius of curvature is determined by the dissimilarity data. The space can be either of positive curvature (spherical) or of negative curvature (hyperbolic). We give an efficient method for solving the spherical and hyperbolic embedding problems on symmetric dissimilarity data. Our approach gives the radius of curvature and a method for approximating the objects as points on a hyperspherical manifold without optimisation. For objects which do not reside exactly on the manifold, we develop a optimisation-based procedure for approximate embedding on a hyperspherical manifold. We use the exponential map between the manifold and its local tangent space to solve the optimisation problem locally in the euclidean tangent space. This process is efficient enough to allow us to embed data sets of several thousand objects. We apply our method to a variety of data including time warping functions, shape similarities, graph similarity and gesture similarity data. In each case the embedding maintains the local structure of the data while placing the points in a metric space. [ABSTRACT FROM AUTHOR]

In this article, a novel approach to schizophrenia classification using magnetic resonance images (MRI) is proposed. The presented method is based on dissimilarity-based classification techniques applied to morphological MRIs and diffusion-weighted images (DWI). Instead of working with features directly, pairwise dissimilarities between expert delineated regions of interest (ROIs) are considered as representations based on which learning and classification can be performed. Experiments are carried out on a set of 59 patients and 55 controls and several pairwise dissimilarity measurements are analyzed. We demonstrate that significant improvements can be obtained when combining over different ROIs and different dissimilarity measures. We show that combining ROIs using the dissimilarity-based representation, we achieve higher accuracies. The dissimilarity-based representation outperforms the feature-based representation in all cases. Best results are obtained by combining the two modalities. In summary, our contribution is threefold: (i) We introduce the usage of dissimilarity-based classification to schizophrenia detection and show that dissimilarity-based classification achieves better results than normal features, (ii) We use dissimilarity combination to achieve better accuracies when carefully selected ROIs and dissimilarity measures are considered, and (iii) We show that by combining multiple modalities we can achieve even better results. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 179-192, 2011 [ABSTRACT FROM AUTHOR]

Abstract: A conventional way to discriminate between objects represented by dissimilarities is the nearest neighbor method. A more efficient and sometimes a more accurate solution is offered by other dissimilarity-based classifiers. They construct a decision rule based on the entire training set, but they need just a small set of prototypes, the so-called representation set, as a reference for classifying new objects. Such alternative approaches may be especially advantageous for non-Euclidean or even non-metric dissimilarities. The choice of a proper representation set for dissimilarity-based classifiers is not yet fully investigated. It appears that a random selection may work well. In this paper, a number of experiments has been conducted on various metric and non-metric dissimilarity representations and prototype selection methods. Several procedures, like traditional feature selection methods (here effectively searching for prototypes), mode seeking and linear programming are compared to the random selection. In general, we find out that systematic approaches lead to better results than the random selection, especially for a small number of prototypes. Although there is no single winner as it depends on data characteristics, the k-centres works well, in general. For two-class problems, an important observation is that our dissimilarity-based discrimination functions relying on significantly reduced prototype sets (3–10% of the training objects) offer a similar or much better classification accuracy than the best k-NN rule on the entire training set. This may be reached for multi-class data as well, however such problems are more difficult. [Copyright &y& Elsevier]

For the sake of classification, spectra are traditionally represented by points in a high-dimensional feature space, spanned by spectral bands. An alternative approach is to represent spectra by dissimilarities to other spectra. This relational representation enables one to treat spectra as connected entities and to emphasize characteristics such as shape, which are difficult to handle in the traditional approach. Several classification methods for relational representations were developed and found to outperform the nearest-neighbor rule. Existing studies focus only on the performance measured by the classification error. However, for real-time spectral imaging applications, classification speed is of crucial importance. Therefore, in this paper, we focus on the computational aspects of the on-line classification of spectra. We show, that classifiers built in dissimilarity spaces may also be applied significantly faster than the nearest-neighbor rule. [Copyright &y& Elsevier]

In this paper, a classification task on dissimilarity representations is considered. A traditional way to discriminate between objects represented by dissimilarities is the nearest neighbor method. It suffers, however, from a number of limitations, i.e., high computational complexity, a potential loss of accuracy when a small set of prototypes is used and sensitivity to noise. To overcome these shortcomings, we propose to use a normal density-based classifier constructed on the same representation. We show that such a classifier, based on a weighted combination of dissimilarities, can significantly improve the nearest neighbor rule with respect to the recognition accuracy and computational effort. [Copyright &y& Elsevier]

Examines the uniform object generation for optimizing one-class classifiers. Identification of target class, a one class of data in one-class classification from the rest of feature space; Proposal of support vector data description to solve the problem of one-class classification; Presentation of results for artificial data and for real world data.

Presents a study which summarized and compared some of the methods used in various stages of a pattern recognition system. Information on pattern recognition; Template matching method used; Statistical approach; Neural networks model; Curse of dimensionality and peaking phenomena.

Provides information on the development of a theoretical framework for combining classifiers which use distinct pattern representations. Comparison of the different classifier combinations schemes; Investigation of the sensitivity analysis of the schemes; Results of the analysis.

Abstract: In multiple-instance learning (MIL), an object is represented as a bag consisting of a set of feature vectors called instances. In the training set, the labels of bags are given, while the uncertainty comes from the unknown labels of instances in the bags. In this paper, we study MIL with the assumption that instances are drawn from a mixture distribution of the concept and the non-concept, which leads to a convenient way to solve MIL as a classifier combining problem. It is shown that instances can be classified with any standard supervised classifier by re-weighting the classification posteriors. Given the instance labels, the label of a bag can be obtained as a classifier combining problem. An optimal decision rule is derived that determines the threshold on the fraction of instances in a bag that is assigned to the concept class. We provide estimators for the two parameters in the model. The method is tested on a toy data set and various benchmark data sets, and shown to provide results comparable to state-of-the-art MIL methods. [Copyright &y& Elsevier]

Abstract: Samples can be measured with different precisions and reliabilities in different experiments, or even within the same experiment. These varying levels of measurement noise may deteriorate the performance of a pattern recognition system, if not treated with care. Here we seek to investigate the benefit of incorporating prior knowledge about measurement noise into system construction. We propose a kernel density classifier which integrates such prior knowledge. Instead of using an identical kernel for each sample, we transform the prior knowledge into a distinct kernel for each sample. The integration procedure is straightforward and easy to interpret. In addition, we show how to estimate the diverse measurement noise levels in a real world dataset. Compared to the basic methods, the new kernel density classifier can give a significantly better classification performance. As expected, this improvement is more obvious for small sample size datasets and large number of features. [Copyright &y& Elsevier]