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An Adaptive Position Synchronization Controller Using Orthogonal Neural Network for 3-DOF Planar Parallel Manipulators Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Local Sensitive Low Rank Matrix Approximation via Nonconvex Optimization

Authors : Chong-Ya Li, Wenzheng Bao, Zhipeng Li, Youhua Zhang, Yong-Li Jiang, Chang-An Yuan

Published in: Intelligent Computing Methodologies

Publisher: Springer International Publishing

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Abstract

The problem of matrix approximation appears ubiquitously in recommendation systems, computer vision and text mining. The prevailing assumption is that the partially observed matrix has a low-rank or can be well approximated by a low-rank matrix. However, this assumption is strictly that the partially observed matrix is globally low rank. In this paper, we propose a local sensitive formulation of matrix approximation which relaxes the global low-rank assumption, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We solve the problem by nonconvex optimization which exhibits superior performance of low rank matrix estimation when compared with convex relaxation. Our experiments show improvements in prediction accuracy over classical approaches for recommendation tasks.

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previous chapter PRACE: A Taxi Recommender for Finding Passengers with Deep Learning Approaches
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Metadata
Title
Local Sensitive Low Rank Matrix Approximation via Nonconvex Optimization
Authors
Chong-Ya Li
Wenzheng Bao
Zhipeng Li
Youhua Zhang
Yong-Li Jiang
Chang-An Yuan
Copyright Year
2017
Book
Intelligent Computing Methodologies

Print ISBN: 978-3-319-63314-5

Electronic ISBN: 978-3-319-63315-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63315-2_67

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