Abstract

In this paper, a new nonlinear subspace learning technique for class-specific data representation based on an optimized class representation is described. An iterative optimization scheme is formulated where both the optimal nonlinear data projection and the optimal class representation are determined at each optimization step. This approach is tested on human face and action recognition problems, where its performance is compared with that of the standard class-specific subspace learning approach, as well as other nonlinear discriminant subspace learning techniques. Experimental results denote the effectiveness of this new approach, since it consistently outperforms the standard one and outperforms other nonlinear discriminant subspace learning techniques in most cases.

Highlights

  • Standard Discriminant Learning techniques, like Linear Discriminant Analysis (LDA) [1, 2], Kernel Discriminant Analysis (KDA) [3], Spectral Regression (KSR) [4] and Class-specific Discriminant Analysis (CSKDA) [5], represent classes by adopting the corresponding class mean vectors

  • An iterative optimization scheme is formulated where both the optimal nonlinear data projection and the optimal class representation are determined at each optimization step

  • In all our experiments we compare the performance of the Class-Specific Reference Discriminant Analysis (CSRDA) with that of the ClassSpecific Kernel Discriminant Analysis (CSKDA) [5], as well as with Kernel Spectral Regression (KSR) [4], Kernel Discriminant Analysis (KDA) [3] and kernel Support Vector Machine (SVM)-based classification [22]

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Summary

Introduction

Standard Discriminant Learning techniques, like Linear Discriminant Analysis (LDA) [1, 2], Kernel Discriminant Analysis (KDA) [3], (kernel) Spectral Regression (KSR) [4] and Class-specific (kernel) Discriminant Analysis (CSKDA) [5], represent classes by adopting the corresponding class mean vectors. They inherently set the assumption that the classes forming the classification problem follow unimodal normal distributions having the same covariance structure [2]. Experiments are conducted on six publicly available datasets, namely the ORL [12], AR [13] and Extended YALE-B [14] for face recognition and Hollywood2 [15], Olympic Sports [16] and ASLAN [17] datasets for human action recognition

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