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Mathematical Problems in Engineering
Volume 2014, Article ID 429451, 21 pages
http://dx.doi.org/10.1155/2014/429451
Research Article

EVD Dualdating Based Online Subspace Learning

1School of Aeronautics and Astronautics, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China
2School of Information Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China

Received 10 April 2014; Accepted 25 June 2014; Published 24 July 2014

Academic Editor: Yan Liang

Copyright © 2014 Bo Jin 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.

Supplementary Material

In many online applications, it is impossible to store the original data because of the limitation of the physical medium and the consideration about efficiency. Described in a mathematical form, this means that the original data matrix A is unobtainable and replaced by its best rank-k approximation which can be calculated by Uk and Λk. Theorem 2 proofs that under the low-rank-plus-shift structure, when A is replaced bybestk(A), the information discarded will also be discarded after EVD dualdating. In other words, EVD dualdating is an optimal rank-k estimator in the sequential usage.

  1. Supplementary Materials