loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Manami Tatsukawa 1 and Mirai Tanaka 2

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

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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.94.150.98

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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 - ICORES; ISBN 978-989-758-285-1; ISSN 2184-4372, SciTePress, 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 - ICORES},
year={2018},
pages={78-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006612100780084},
isbn={978-989-758-285-1},
issn={2184-4372},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Operations Research and Enterprise Systems - ICORES
TI - Box Constrained Low-rank Matrix Approximation with Missing Values
SN - 978-989-758-285-1
IS - 2184-4372
AU - Tatsukawa, M.
AU - Tanaka, M.
PY - 2018
SP - 78
EP - 84
DO - 10.5220/0006612100780084
PB - SciTePress