Document Detail


Constrained Non-negative Matrix Factorization for Image Representation.
MedLine Citation:
PMID:  22064797     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Non-negative matrix factorization (NMF) is a popular technique for finding parts-based, linear representations of non-negative data. It has been successfully applied in wide range of applications such as pattern recognition, information retrieval and computer vision. However, NMF is essentially an unsupervised method and can not make use of label information. In this paper, we propose a novel semi-supervised matrix decomposition method, called Constrained Non-negative Matrix Factorization (CNMF), which incorporates the label information as additional constraints. Specifically, we show how explicitly combining label information improves the discriminating power of the resulting matrix decomposition. We explore the proposed CNMF method with two cost function formulations and provide the corresponding update solutions for the optimization problems. Empirical experiments demonstrate the effectiveness of our novel algorithm in comparison to the state-of-the-art approaches through a set of evaluations based on real world applications.
Authors:
Haifeng Liu; Zhaohui Wu; Deng Cai; Thomas S Huang
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-11-1
Journal Detail:
Title:  IEEE transactions on pattern analysis and machine intelligence     Volume:  -     ISSN:  1939-3539     ISO Abbreviation:  -     Publication Date:  2011 Nov 
Date Detail:
Created Date:  2011-11-8     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9885960     Medline TA:  IEEE Trans Pattern Anal Mach Intell     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Zhejiang University, Hangzhou.
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