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Paper Abstract and Keywords
Presentation 2018-03-05 17:00
Transformed Multiple Matrix Factorization: Towards Utilizing Heterogeneous Auxiliary Information
Taira Tsuchiya (Waseda Univ.), Tomoharu Iwata (NTT), Tetsuji Ogawa (Waseda Univ.) IBISML2017-96
Abstract (in Japanese) (See Japanese page) 
(in English) Matrix factorization is widely used for a variety of fields, such as computer vision, document analysis, signal processing, and collaborative filtering. Matrix factorization acquires latent representations for rows and columns so that their inner products can represent elements of a target matrix. Auxiliary matrices with different properties are often available. Multiple matrix factorization exploits these auxiliary matrices to make matrix factorization more robust against the sparseness of the target matrix. Multiple matrix factorization, however, fails to utilize auxiliary information when these matrices have heterogeneous relationships against the target matrix because it learns the identical latent representations for multiple matrices, resulting in modeling linear relationships between matrices. In the present paper, transformed multiple matrix factorization is proposed to tackle this problem. The proposed method assumes that common latent representations, which are newly introduced, are shared across different matrices, but they are transformed by different non-linear functions to obtain latent representations for each matrix. The proposed method can therefore handle heterogeneous relationships among different matrices. Experiments conducted on three real-world datasets demonstrated that the proposed method outperformed vanilla matrix factorization and multiple matrix factorization.
Keyword (in Japanese) (See Japanese page) 
(in English) matrix factorizatoin / multi-task learning / transfer learning / neural networks / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 475, IBISML2017-96, pp. 41-48, March 2018.
Paper # IBISML2017-96 
Date of Issue 2018-02-26 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IBISML2017-96

Conference Information
Committee IBISML  
Conference Date 2018-03-05 - 2018-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Nishijin Plaza, Kyushu University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Statisitical Mathematics, Machine Learning, Data Mining, etc. 
Paper Information
Registration To IBISML 
Conference Code 2018-03-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Transformed Multiple Matrix Factorization: Towards Utilizing Heterogeneous Auxiliary Information 
Sub Title (in English)  
Keyword(1) matrix factorizatoin  
Keyword(2) multi-task learning  
Keyword(3) transfer learning  
Keyword(4) neural networks  
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1st Author's Name Taira Tsuchiya  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Tomoharu Iwata  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Tetsuji Ogawa  
3rd Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2018-03-05 17:00:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2017-96 
Volume (vol) vol.117 
Number (no) no.475 
Page pp.41-48 
#Pages 8 
Date of Issue 2018-02-26 (IBISML) 

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