Graph Regularized Non-negative Matrix Factorization for Data Representation

Cai, Deng; He, Xiaofei; Han, Jiawei

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

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

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

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

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