DocumentCode :
3491933
Title :
Two Dimensional Nonnegative Matrix Factorization
Author :
Gu, Quanquan ; Zhou, Jie
fYear :
2009
fDate :
7-10 Nov. 2009
Firstpage :
2069
Lastpage :
2072
Abstract :
Nonnegative Matrix Factorization (NMF) has been widely used in computer vision and pattern recognition. It aims to find two nonnegative matrices whose product can well approximate the original matrix, which naturally leads to parts-based representation. In this paper, we propose a Two Dimensional Nonnegative Matrix Factorization (2DNMF), specifically for a sequence of matrices. In contrast to NMF which applies for only a single matrix, and finds only one base matrix, 2DNMF aims to find two base matrices to represent the input matrices in a low dimensional matrix subspace. It not only inherits the advantages of NMF, but also owns the properties low computational complexity, as well as high recognition accuracy. Experiments on benchmark image recognition data sets illustrate that the proposed method is very effective and efficient.
Keywords :
image recognition; matrix decomposition; benchmark image recognition data sets; computational complexity; computer vision; low dimensional matrix subspace; pattern recognition; two dimensional nonnegative matrix factorization; Automation; Computational complexity; Computer vision; Image recognition; Information science; Intelligent systems; Laboratories; Pattern recognition; Principal component analysis; Sparse matrices; Feature Extraction; Nonnegative Matrix Factorization; Two Dimensional;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2009 16th IEEE International Conference on
Conference_Location :
Cairo
ISSN :
1522-4880
Print_ISBN :
978-1-4244-5653-6
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2009.5414303
Filename :
5414303
Link To Document :
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