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ICML
2007
IEEE

Relational clustering by symmetric convex coding

9 years 8 months ago
Relational clustering by symmetric convex coding
Relational data appear frequently in many machine learning applications. Relational data consist of the pairwise relations (similarities or dissimilarities) between each pair of implicit objects, and are usually stored in relation matrices and typically no other knowledge is available. Although relational clustering can be formulated as graph partitioning in some applications, this formulation is not adequate for general relational data. In this paper, we propose a general model for relational clustering based on symmetric convex coding. The model is applicable to all types of relational data and unifies the existing graph partitioning formulation. Under this model, we derive two alternative bound optimization algorithms to solve the symmetric convex coding under two popular distance functions, Euclidean distance and generalized I-divergence. Experimental evaluation and theoretical analysis show the effectiveness and great potential of the proposed model and algorithms.
Bo Long, Zhongfei (Mark) Zhang, Xiaoyun Wu, Philip
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Bo Long, Zhongfei (Mark) Zhang, Xiaoyun Wu, Philip S. Yu
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