Affiliation:
1. College of Computer and Information Science, Fujian Agriculture and Forestry University & Key Laboratory for Ecology and Resources Statistics of Fujian Province, Fuzhou, P. R. China
2. College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, P. R. China
Abstract
Principal component analysis (PCA) has been a powerful tool for high-dimensional data analysis. It is usually redesigned to the incremental PCA algorithm for processing streaming data. In this paper, we propose a subspace type incremental two-dimensional PCA algorithm (SI2DPCA) derived from an incremental updating of the eigenspace to compute several principal eigenvectors at the same time for the online feature extraction. The algorithm overcomes the problem that the approximate eigenvectors extracted from the traditional incremental two-dimensional PCA algorithm (I2DPCA) are not mutually orthogonal, and it presents more efficiently. In numerical experiments, we compare the proposed SI2DPCA with the traditional I2DPCA in terms of the accuracy of computed approximations, orthogonality errors, and execution time based on widely used datasets, such as FERET, Yale, ORL, and so on, to confirm the superiority of SI2DPCA.
Funder
National Natural Science Foundation of China
the research fund for distinguished young scholars of Fujian Agriculture and Forestry University
Cited by
3 articles.
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