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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yong-Deok Kim Seungjin Choi |
| Copyright Year | 2007 |
| Description | Author affiliation: Dept. of Comput. Sci., POSTECH, South Korea (Yong-Deok Kim; Seungjin Choi) |
| Abstract | Nonnegative matrix factorization (NMF) is a widely-used method for multivariate nonnegative data analysis, due to its ability to learn a parts-based representation. However, the standard NMF algorithm does not always find spatially localized basis images in practice, unless sparseness constraints are employed. In this paper we present a method of structured initialization which enables the standard NMF algorithm to find spatially localized basis images. The initialization method is based on the hierarchical clustering of attributes through a similarity measure reflecting 'closeness to rank-one'. Numerical experiments with face image data, confirm the validity of our initialization method. |
| File Size | 1185688 |
| File Format | |
| ISBN | 1424407273 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2007.366291 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-15 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Clustering algorithms Matrix decomposition Convergence Data analysis Principal component analysis Independent component analysis Computer science Feature extraction Pattern clustering Pattern classification unsupervised learning matrix decomposition pattern clustering methods pattern classification |
| Content Type | Text |
| Resource Type | Article |
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