Constrained Projection Approximation Algorithms for Principal Component Analysis

被引：0
|
作者：
Seungjin Choi
Jong-Hoon Ahn
Andrzej Cichocki
机构：
[1] Pohang University of Science and Technology,Department of Computer Science
[2] Pohang University of Science and Technology,Department of Physics
[3] Brain Science Institute,Advanced Brain Signal Processing Lab
[4] RIKEN,undefined
来源：
Neural Processing Letters | 2006年 / 24卷
关键词：
natural power iteration; principal component analysis; projection approximation; reconstruction error; subspace analysis;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we introduce a new error measure, integrated reconstruction error (IRE) and show that the minimization of IRE leads to principal eigenvectors (without rotational ambiguity) of the data covariance matrix. Then, we present iterative algorithms for the IRE minimization, where we use the projection approximation. The proposed algorithm is referred to as COnstrained Projection Approximation (COPA) algorithm and its limiting case is called COPAL. Numerical experiments demonstrate that these algorithms successfully find exact principal eigenvectors of the data covariance matrix.
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页码：53 / 65
页数：12
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