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Mathematical Problems in Engineering
Volume 2015, Article ID 329753, 8 pages
http://dx.doi.org/10.1155/2015/329753
Research Article

Multiview Sample Classification Algorithm Based on L1-Graph Domain Adaptation Learning

School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China

Received 14 November 2014; Revised 4 February 2015; Accepted 7 February 2015

Academic Editor: Gerhard-Wilhelm Weber

Copyright © 2015 Huibin Lu 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

In the case of multiview sample classification with different distribution, training and testing samples are from different domains. In order to improve the classification performance, a multiview sample classification algorithm based on L1-Graph domain adaptation learning is presented. First of all, a framework of nonnegative matrix trifactorization based on domain adaptation learning is formed, in which the unchanged information is regarded as the bridge of knowledge transformation from the source domain to the target domain; the second step is to construct L1-Graph on the basis of sparse representation, so as to search for the nearest neighbor data with self-adaptation and preserve the samples and the geometric structure; lastly, we integrate two complementary objective functions into the unified optimization issue and use the iterative algorithm to cope with it, and then the estimation of the testing sample classification is completed. Comparative experiments are conducted in USPS-Binary digital database, Three-Domain Object Benchmark database, and ALOI database; the experimental results verify the effectiveness of the proposed algorithm, which improves the recognition accuracy and ensures the robustness of algorithm.