A FAST INCREMENTAL MULTILINEAR PRINCIPAL COMPONENT ANALYSIS ALGORITHM

被引：0

作者：

Wang, Jin ^{[1
]}

Barreto, Armando ^{[1
]}

Rishe, Naphtali ^{[2
]}

Andrian, Jean ^{[1
]}

Adjouadi, Malek ^{[1
]}

机构：

[1] Florida Int Univ, Dept Elect & Comp Engn, Miami, FL 33199 USA

[2] Florida Int Univ, Sch Comp & Informat Sci, Miami, FL 33199 USA

来源：

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2011年 / 7卷 / 10期

基金：

美国国家科学基金会;

关键词：

Multilinear principal component analysis; Fast principal component analysis; Incremental subspace learning; Sequential Karhunen-Loeve algorithm; Mean update; APPROXIMATION; DECOMPOSITION; TRACKING; MACHINE;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：6019 / 6040

页数：22

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