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Mathematical Problems in Engineering - Volume 2014 2014, Article ID 819758, 10 pages -

Research Article

College of Information Science and Technology, Chengdu University, Chengdu 610106, China

Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province, Chengdu 610106, China

School of Computer Science, Sichuan University, Chengdu 610065, China

Received 5 July 2013; Revised 21 September 2013; Accepted 22 September 2013; Published 30 January 2014

Academic Editor: Praveen Agarwal

Copyright © 2014 Chang Liu 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.


To overcome the shortcomings of traditional dimensionality reduction algorithms, incremental tensor principal component analysis ITPCA based on updated-SVD technique algorithm is proposed in this paper. This paper proves the relationship between PCA, 2DPCA, MPCA, and the graph embedding framework theoretically and derives the incremental learning procedure to add single sample and multiple samples in detail. The experiments on handwritten digit recognition have demonstrated that ITPCA has achieved better recognition performance than that of vector-based principal component analysis PCA, incremental principal component analysis IPCA, and multilinear principal component analysis MPCA algorithms. At the same time, ITPCA also has lower time and space complexity.

Author: Chang Liu, Tao Yan, WeiDong Zhao, YongHong Liu, Dan Li, Feng Lin, and JiLiu Zhou



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