On Application of Block Kaczmarz Methods in Matrix Factorization

Edwin Chau; Jamie Haddock

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arXiv · 2010.10635

On Application of Block Kaczmarz Methods in Matrix Factorization

Edwin Chau·Jamie Haddock

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Abstract

Matrix factorization techniques compute low-rank product approximations of high dimensional data matrices and as a result, are often employed in recommender systems and collaborative filtering applications. However, many algorithms for this task utilize an exact least-squares solver whose computation is time consuming and memory-expensive. In this paper we discuss and test a block Kaczmarz solver that replaces the least-squares subroutine in the common alternating scheme for matrix factorization. This variant trades a small increase in factorization error for significantly faster algorithmic performance. In doing so we find block sizes that produce a solution comparable to that of the least-squares solver for only a fraction of the runtime and working memory requirement.

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Edwin Chau, Jamie Haddock. 2020-10-20. On Application of Block Kaczmarz Methods in Matrix Factorization. https://arxiv.org/abs/2010.10635

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