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Authors: Manami Tatsukawa 1 and Mirai Tanaka 2

Affiliations: 1 Tokyo Institute of Technology, Japan ; 2 The Institute of Statistical Mathematics, Japan

ISBN: 978-989-758-285-1

Keyword(s): Low-Rank Matrix Approximation, Missing Data, Matrix Completion with Noise, Principal Component Analysis with Missing Values, Collaborative Filtering, Block Coordinate Descent Method.

Abstract: In this paper, we propose a new low-rank matrix approximation model for completing a matrix with missing values. Our proposed model contains a box constraint that arises from the context of collaborative filtering. Although it is unfortunately NP-hard to solve our model with high accuracy, we can construct a practical algorithm to obtain a stationary point. Our proposed algorithm is based on alternating minimization and converges to a stationary point under a mild assumption.

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Paper citation in several formats:
Tatsukawa, M. and Tanaka, M. (2018). Box Constrained Low-rank Matrix Approximation with Missing Values.In Proceedings of the 7th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-285-1, pages 78-84. DOI: 10.5220/0006612100780084

@conference{icores18,
author={Manami Tatsukawa. and Mirai Tanaka.},
title={Box Constrained Low-rank Matrix Approximation with Missing Values},
booktitle={Proceedings of the 7th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2018},
pages={78-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006612100780084},
isbn={978-989-758-285-1},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Box Constrained Low-rank Matrix Approximation with Missing Values
SN - 978-989-758-285-1
AU - Tatsukawa, M.
AU - Tanaka, M.
PY - 2018
SP - 78
EP - 84
DO - 10.5220/0006612100780084

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