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Title: Compact Matrix Factorization with Dependent Subspaces
Full text: PDF
Authors: Larsson, Viktor and Olsson, Carl
Year: 2017
Document Type:Conference Paper
Conference: CVPR
Conference location: Hawaii
Status: In Press
Refereed: Yes
BibTeX item:BibTeX
Abstract: Traditional matrix factorization methods approximate high dimensional data with a low dimensional subspace. This imposes constraints on the matrix elements which allow for estimation of missing entries. A lower rank provides stronger constraints and makes estimation of the missing entries less ambiguous at the cost of measurement fit. In this paper we propose a new factorization model that further constrains the matrix entries. Our approach can be seen as a unification of traditional low-rank matrix factorization and the more recent union-of-subspace approach. It adaptively finds clusters that can be modeled with low dimensional local subspaces and simultaneously uses a global rank constraint to capture the overall scene interactions. For inference we use an energy that penalizes a trade-off between data fit and degrees-of-freedom of the resulting factorization. We show qualitatively and quantitatively that regularizing both local and global dynamics yields significantly improved missing data estimation.




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