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Matrix Decomposition-Based Dimensionality Reduction on Graph Data

Matrix Decomposition-Based Dimensionality Reduction on Graph Data

Hiroto Saigo, Koji Tsuda
Copyright: © 2012 |Pages: 25
ISBN13: 9781613500538|ISBN10: 161350053X|EISBN13: 9781613500545
DOI: 10.4018/978-1-61350-053-8.ch011
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MLA

Saigo, Hiroto, and Koji Tsuda. "Matrix Decomposition-Based Dimensionality Reduction on Graph Data." Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, IGI Global, 2012, pp. 260-284. https://doi.org/10.4018/978-1-61350-053-8.ch011

APA

Saigo, H. & Tsuda, K. (2012). Matrix Decomposition-Based Dimensionality Reduction on Graph Data. In S. Sakr & E. Pardede (Eds.), Graph Data Management: Techniques and Applications (pp. 260-284). IGI Global. https://doi.org/10.4018/978-1-61350-053-8.ch011

Chicago

Saigo, Hiroto, and Koji Tsuda. "Matrix Decomposition-Based Dimensionality Reduction on Graph Data." In Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, 260-284. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-053-8.ch011

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Abstract

Graph is a mathematical framework that allows us to represent and manage many real-world data such as relational data, multimedia data and biomedical data. When each data point is represented as a graph and we are given a number of graphs, a task is to extract a few common patterns that capture the property of each population. A frequent graph mining algorithm such as AGM, gSpan and Gaston can enumerate all the frequent patterns in graph data, however, the number of patterns grows exponentially, therefore it is essential to output only discriminative patterns. There are many existing researches on this topic, but this chapter focus on the use of matrix decomposition techniques, and explains the two general cases where either i) no target label is available, or ii) target label is available for each data point. The reuslting method is a branch and bound pattern mining algorithm with efficient pruning condition, and we evaluate its effectiveness on cheminformatics data.

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