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Applied Computational Intelligence and Soft Computing
Volume 2016 (2016), Article ID 2783568, 9 pages
http://dx.doi.org/10.1155/2016/2783568
Research Article

Low-Rank Kernel-Based Semisupervised Discriminant Analysis

1School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China
2Key Lab of Big Data Computation of Hebei Province, Tianjin 300401, China

Received 16 April 2016; Accepted 14 June 2016

Academic Editor: Yu Cao

Copyright © 2016 Baokai Zu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Semisupervised Discriminant Analysis (SDA) aims at dimensionality reduction with both limited labeled data and copious unlabeled data, but it may fail to discover the intrinsic geometry structure when the data manifold is highly nonlinear. The kernel trick is widely used to map the original nonlinearly separable problem to an intrinsically larger dimensionality space where the classes are linearly separable. Inspired by low-rank representation (LLR), we proposed a novel kernel SDA method called low-rank kernel-based SDA (LRKSDA) algorithm where the LRR is used as the kernel representation. Since LRR can capture the global data structures and get the lowest rank representation in a parameter-free way, the low-rank kernel method is extremely effective and robust for kinds of data. Extensive experiments on public databases show that the proposed LRKSDA dimensionality reduction algorithm can achieve better performance than other related kernel SDA methods.