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Graph Regularized Non-negative Matrix Factorization for Data Representation
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http://hdl.handle.net/2142/11453
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| Title: | Graph Regularized Non-negative Matrix Factorization for Data Representation |
| Author(s): | Cai, Deng; He, Xiaofei; Han, Jiawei |
| Subject(s): |
database
information systems |
| Abstract: | Recently Non-negative Matrix Factorization (NMF) has received a lot of attentions in information retrieval, computer vision and pattern recognition. NMF aims to find two non-negative matrices whose product can well approximate the original matrix. The sizes of these two matrices are usually smaller than the original matrix. This results in a compressed version of the original data matrix. The solution of NMF yields a natural parts-based representation for the data. When NMF is applied for data representation, a major disadvantage is that it fails to consider the geometric structure in the data. In this paper, we develop a graph based approach for parts-based data representation in order to overcome this limitation. We construct an affinity graph to encode the geometrical information and seek a matrix factorization which respects the graph structure. We demonstrate the success of this novel algorithm by applying it on real world problems. |
| Issue Date: | 2008-02 |
| Genre: | Technical Report |
| Type: | Text |
| URI: | http://hdl.handle.net/2142/11453 |
| Other Identifier(s): | UIUCDCS-R-2008-2962 |
| Rights Information: | You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s). |
| Date Available in IDEALS: | 2009-04-22 |
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