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2010
IEEE

Edge Weight Regularization over Multiple Graphs for Similarity Learning

11 years 7 months ago
Edge Weight Regularization over Multiple Graphs for Similarity Learning
The growth of the web has directly influenced the increase in the availability of relational data. One of the key problems in mining such data is computing the similarity between objects with heterogeneous feature types. For example, publications have many heterogeneous features like text, citations, authorship information, venue information, etc. In most approaches, similarity is estimated using each feature type in isolation and then combined in a linear fashion. However, this approach does not take advantage of the dependencies between the different feature spaces. In this paper, we propose a novel approach to combine the different sources of similarity using a regularization framework over edges in multiple graphs. We show that the objective function induced by the framework is convex. We also propose an efficient algorithm using coordinate descent [1] to solve the optimization problem. We extrinsically evaluate the performance of the proposed unified similarity measure on two diff...
Pradeep Muthukrishnan, Dragomir R. Radev, Qiaozhu
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICDM
Authors Pradeep Muthukrishnan, Dragomir R. Radev, Qiaozhu Mei
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