Nonnegative matrix factorization with bounded total variational regularization for face recognition

被引：18

作者：

Yin, Haiqing ^{[1
]}

Liu, Hongwei ^{[1
]}

机构：

[1] Xidian Univ, Dept Appl Math, Xian 710071, Peoples R China

来源：

PATTERN RECOGNITION LETTERS | 2010年 / 31卷 / 16期

基金：

中国国家自然科学基金;

关键词：

Nonnegative matrix factorization; Total variation regularization; Face recognition;

D O I：

10.1016/j.patrec.2010.08.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Nonnegative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of nonnegative data based on minimizing least square error (L-2 norm). However it has been observed that the proper norm for images is the bounded total variation (TV) norm other than the L-2 norm. The space of functions of bounded TV allows discontinuous solution and plays an important role in image processing. In this paper, we propose a new NMF model with bounded TV regularization for identifying discriminate representation of image patterns. We provide a simple update rule for computing the factorization and give supporting theoretical analysis. Finally, we perform a series of numerical experiments to show evidence of the good behavior of the numerical scheme. Crown Copyright (C) 2010 Published by Elsevier B.V. All rights reserved.

引用

页码：2468 / 2473

页数：6

共 21 条

[1] RECOGNITION-BY-COMPONENTS - A THEORY OF HUMAN IMAGE UNDERSTANDING [J].

BIEDERMAN, I .

PSYCHOLOGICAL REVIEW, 1987, 94 (02) :115-147

[2] Orthogonal laplacianfaces for face recognition [J].

Cai, Deng ;

He, Xiaofei ;

Han, Jiawei ;

Zhang, Hong-Jiang .

IEEE TRANSACTIONS ON IMAGE PROCESSING, 2006, 15 (11) :3608-3614

[3] Tikhonov regularization of large linear problems [J].

Calvetti, D ;

Reichel, L .

BIT NUMERICAL MATHEMATICS, 2003, 43 (02) :263-283

[4]

Chan T, 2006, HANDBOOK OF MATHEMATICAL MODELS IN COMPUTER VISION, P17, DOI 10.1007/0-387-28831-7_2

[5] INDEPENDENT COMPONENT ANALYSIS, A NEW CONCEPT [J].

COMON, P .

SIGNAL PROCESSING, 1994, 36 (03) :287-314

[6]

Feng T., 2002, P 2 INT C DEV LEARN

[7] Introducing a weighted non-negative matrix factorization for image classification [J].

Guillamet, D ;

Vitrià, J ;

Schiele, B .

PATTERN RECOGNITION LETTERS, 2003, 24 (14) :2447-2454

[8]

Hoyer PO, 2004, J MACH LEARN RES, V5, P1457

[9]

Hoyer PO, 2002, NEURAL NETWORKS FOR SIGNAL PROCESSING XII, PROCEEDINGS, P557, DOI 10.1109/NNSP.2002.1030067

[10] Choosing regularization parameters in iterative methods for ill-posed problems [J].

Kilmer, ME ;

O'Leary, DP .

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 2001, 22 (04) :1204-1221

← 1 2 3 →