Discriminant Independent Component Analysis

Abstract

A conventional linear model based on Negentropy maximization extracts statistically independent latent variables which may not be optimal to give a discriminant model with good classification performance. In this paper, a single-stage linear semisupervised extraction of discriminative independent features is proposed. Discriminant independent component analysis (dICA) presents a framework of linearly projecting multivariate data to a lower dimension where the features are maximally discriminant with minimal redundancy. The optimization problem is formulated as the maximization of linear summation of Negentropy and weighted functional measure of classification. Motivated by independence among extracted features, Fisher linear discriminant is used as the functional measure of classification. Experimental results show improved classification performance when dICA features are used for recognition tasks in comparison to unsupervised (principal component analysis and ICA) and supervised feature extraction techniques like linear discriminant analysis (LDA), conditional ICA, and those based on information theoretic learning approaches. dICA features also give reduced data reconstruction error in comparison to LDA and ICA method based on Negentropy maximization.

Dimensionality Reduction using Hybrid Support Vector Machine and Discriminant Independent Component Analysis for Hyperspectral Image

“…In the Discriminant independent component analysis (DICA) method, multivariate data with lower dimensions and independent features are obtained through Negentropy maximization [4]. In DICA, the Fisher criterion and the sum of marginal negentropy independent features are extracted by maximizing simultaneously.…”

“…The effectiveness of observations and dimensional space of reduction is measured by the criteria defined in various dimensional data reduction algorithms. Many techniques to reduce dimension data are Principal Component Analysis, Linear Discriminant Analysis [2], Independent Component Analysis (ICA) [3], and Discriminant Independent Component Analysis [4] which is an extension of the ICA.…”

“…ICA obtains a projection vector that is statistically independent of observational data through criteria representing independence such as Kullback-Leibler (KL) divergence, mutual information (MI) and correlation. DICA in [4] represents a methodology for combining both ICA and Fisher Linear Discriminant functions in order to construct feature extraction methods through variable projection where variables are projected through maximum independence. In the previous experiment we use independent component analysis with particle swarm optimization as contrast function as a dataset feature reduction [10].…”

Mixture Subclass Discriminant Analysis Link to Restricted Gaussian Model and Other Generalizations

“…Several methods have been proposed to deal with this problem, including PCA+LDA [6], MMC LDA [7], dICA [8], and others. b) LDA faces difficulties in deriving a discriminant subspace when the classes are not linearly separable (a problem called hereafter nonlinearity problem).…”

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine

“…Recently a scheme called discriminative independent components analysis (dICA) has been proposed, which combines the strengths of unsupervised learning of ICA and supervised learning of LDA, and enhances the generalization ability of ICAbased representation methods [44,45]. However, the method has been applied only to face recognition problems.…”