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A FAST INCREMENTAL MULTILINEAR PRINCIPAL COMPONENT ANALYSIS ALGORITHM

This study establishes the mathematical foundation for a fast incremental multilinear method which combines the traditional sequential Karhunen-Loeve (SKL) algorithm with the newly developed incremental modified fast Principal Component Analysis algorithm (IMFPCA). In accordance with the characteristics of the data structure, the proposed algorithm achieves both computational efficiency and high accuracy for incremental subspace updating. Moreover, the theoretical foundation is analyzed in detail as to the competing aspects of IMFPCA and SKL with respect to the different data unfolding schemes. Besides the general experiments designed to test the performance of the proposed algorithm, incremental face recognition system was developed as a real-world application for the proposed algorithm.