2013 LocalLowRankMatrixApproximation

• (Lee et al., 2013) ⇒ Joonseok Lee, Seungyeon Kim, Guy Lebanon, and Yoram Singer. (2013). “Local Low-rank Matrix Approximation.” In: Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28.

Subject Headings: Local Low-Rank Matrix Approximation (LLORMA).

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Local+Low-rank+Matrix+Approximation
• http://dl.acm.org/citation.cfm?id=3042817.3042903&preflayout=flat#citedby

Quotes

Abstract

Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is locally of low-rank, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We analyze the accuracy of the proposed local low-rank modeling. Our experiments show improvements in prediction accuracy over classical approaches for recommendation tasks.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 LocalLowRankMatrixApproximation	Yoram Singer Guy Lebanon Joonseok Lee Seungyeon Kim			Local Low-rank Matrix Approximation						2013