Classification studies with high-dimensional measurements and relatively small sample sizes are increasingly common. Prospective analysis of the role of sample sizes in the performance of such studies is important for study design and interpretation of results, but the complexity of typical pattern discovery methods makes this problem challenging. The approach developed here combines Monte Carl...

when the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. in this paper, we propose a supervised feature extraction method based on discriminant analysis (da) which uses the first principal component (pc1) to weight the scatter matrices. the proposed method, called da-pc1, copes with the small sample size problem and has...

This paper presents a new offline face recognition system. The proposed system is built on one dimensional left-toright Hidden Markov Models (1D-HMMs). Facial image features are extracted using Gabor wavelets. The dimensionality of these features is reduced using the Fisher’s Discriminant Analysis method to keep only the most relevant information. Unlike existing techniques using 1D-HMMs, in cl...

introduction: brain computer interface (bci) systems based on movement imagination (mi) are widely used in recent decades. separate feature extraction methods are employed in the mi data sets and classified in virtual reality (vr) environments for real-time applications. methods: this study applied wide variety of features on the recorded data using linear discriminant analysis (lda) classifier...

This paper addresses the issue of selecting features from a given wavelet packet subband decomposition that are most useful for texture classification in an image. A functional measure based on Kullback-Leibler distance is proposed as a way to select most discriminant subbands. Experimental results show a superior performance in terms of classification error rates.

Sparse discriminant methods based on independence rules, such as the nearest shrunken centroids classifier (Tibshirani et al., 2002) and features annealed independence rules (Fan & Fan, 2008), have been proposed as computationally attractive tools for feature selection and classification with high-dimensional data. A fundamental drawback of these rules is that they ignore correlations among fea...

Linear discriminant analysis (LDA) is one of the most effective feature extraction methods in statistical pattern recognition, which extracts the discriminant features by maximizing the so-called Fisher’s criterion that is defined as the ratio of between-class scatter matrix to within-class scatter matrix. However, classification of high-dimensional statistical data is usually not amenable to s...

This paper 1 proposes a method to extract nonlinear discriminant features from given input measurements by using outputs of multilayer Perceptron (MLP). Linear Discriminant Analysis (LDA) is one of the best known methods to construct linear features which are suitable for class discrimination. Otsu showed that LDA can be extended to nonlinear if we can estimate Bayesian a posteriori probabiliti...

www.PosterPresentations.com • This paper presents a sparse representation based complete kernel marginal Fisher analysis (SCMFA) framework for categorizing fine art images. • First, we introduce several Fisher vector based features for feature extraction so as to extract and encode important discriminatory information of the painting image. • Second, we propose a complete marginal Fisher analys...

This letter investigates a link-analysis variant of discriminant analysis for projecting nodes of a (partially) labeled graph in a low-dimensional subspace and extracting discriminant node features. Basically, it corresponds to a kernel discriminant analysis computed from a kernel on a graph together with a class betweenness measure. As for standard discriminant analysis, the projected nodes ar...