A New Incremental PCA Algorithm With Application to Visual Learning and Recognition

Authors:
Dong Huang;Zhang Yi;Xiaorong Pu
Affiliations:
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, People's Republic of China 610054;Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, People's Republic of China 610065;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, People's Republic of China 610054
Venue:
Neural Processing Letters
Year:
2009

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Abstract

This paper proposes a new mean-shifting Incremental PCA (IPCA) method based on the autocorrelation matrix. The dimension of the updated matrix remains constant instead of increasing with the number of input data points. Comparing to some previous batch and iterative PCA algorithms, the proposed IPCA requires lower computational time and storage capacity owing to the two transformations designed. The experiment results show the efficiency and accuracy of the proposed IPCA method in applications of the on-line visual learning and recognition.