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2015 | OriginalPaper | Chapter

A Scalable and Feasible Matrix Completion Approach Using Random Projection

Author : Xiang Cao

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

The low rank matrix completion problem has attracted great attention and been widely studied in collaborative filtering and recommendation systems. The rank minimization problem is NP-hard, so the problem is usually relaxed into a matrix nuclear norm minimization. However, the usage is limited in scability due to the high computational complexity of singular value decomposition (SVD). In this paper we introduce a random projection to handle this limitation. In particular, we use a randomized SVD to accelerate the classical Soft-Impute algorithm for the matrix completion problem. The empirical results show that our approach is more efficient while achieving almost same performance.

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previous chapter Proposal of Channel Prediction by Complex-Valued Neural Networks that Deals with Polarization as a Transverse Wave Entity
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Metadata
Title
A Scalable and Feasible Matrix Completion Approach Using Random Projection
Author
Xiang Cao
Copyright Year
2015
Book
Neural Information Processing

Print ISBN: 978-3-319-26554-4

Electronic ISBN: 978-3-319-26555-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26555-1_62

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