Convex Collective Matrix Factorization

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Guillaume Bouchard, Dawei Yin, Shengbo Guo ;
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, PMLR 31:144-152, 2013.

Abstract

In many applications, multiple interlinked sources of data are available and they cannot be represented by a single adjacency matrix, to which large scale factorization method could be applied. Collective matrix factorization is a simple yet powerful approach to jointly factorize multiple matrices, each of which represents a relation between two entity types. Existing algorithms to estimate parameters of collective matrix factorization models are based on non-convex formulations of the problem; in this paper, a convex formulation of this approach is proposed. This enables the derivation of large scale algorithms to estimate the parameters, including an iterative eigenvalue thresholding algorithm. Numerical experiments illustrate the benefits of this new approach.

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