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|Title:||An integrated algorithm of incremental and robust PCA|
|Keywords:||Principal component analysis;Image processing;Eigenvalues and eigenfunctions;Pattern recognition|
|Citation:||Proceedings of IEEE International Conference on Image Processing, (ICIP), 14-17 September, 1: pp. 245 - 248, (2003)|
|Abstract:||Principal component analysis (PCA) is a well-established technique in image processing and pattern recognition. Incremental PCA and robust PCA are two interesting problems with numerous potential applications. However, these two issues have only been separately addressed in the previous studies. In this paper, we present a novel algorithm for incremental and robust PCA by seamlessly integrating the two issues together. The proposed algorithm has the advantages of both incremental PCA and robust PCA. Moreover, unlike most M-estimation based robust algorithms, it is computational efficient. Experimental results on dynamic background modelling are provided to show the performance of the algorithm with a comparison to the conventional batch-mode and nonrobust algorithms.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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