University of Illinois Urbana-Champaign

Graph Regularized Non-negative Matrix Factorization for Data Representation

Cai, Deng; He, Xiaofei; Han, Jiawei

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Graph Regularized Non-negative Matrix Factorization for Data Representation.pdf

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https://hdl.handle.net/2142/11453

Description

Title
Graph Regularized Non-negative Matrix Factorization for Data Representation
Author(s)
  • Cai, Deng
  • He, Xiaofei
  • Han, Jiawei
Issue Date
2008-02
Keyword(s)
  • database
  • information systems
Date of Ingest
2009-04-22T21:38:41Z
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.
Type of Resource
text
Genre of Resource
Technical Report
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http://hdl.handle.net/2142/11453
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