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Paper Abstract and Keywords
Presentation 2022-01-18 14:40
Domain Adaptation with Optimal Transport for Extended Variable Space
Toshimitsu Atiake (ISM), Hideitsu Hino (ISM/RIKEN) IBISML2021-27
Abstract (in Japanese) (See Japanese page) 
(in English) Domain adaptation aims to transfer knowledge of labeled instances obtained from a source domain to a target domain to fill the gap between the domains. Most domain adaptation methods assume that the source and target domains have the same dimensionality. Methods that are applicable when the number of variables is different in each domain have rarely been studied, especially when no label information is given for the test data obtained from the target domain. In this paper, it is assumed that common variables exist in both domains and that extra (new additional) variables are observed in the target domain; hence, the dimensionality of the target domain is higher than that of the source domain. To leverage the homogeneity of the common variables, the adaptation between these source and target domains is formulated as an optimal transport (OT) problem. In addition, a learning bound in the target domain for the proposed OT-based method is derived. The proposed algorithm is validated using both simulated and real-world data.
Keyword (in Japanese) (See Japanese page) 
(in English) heterogeneous domain adaptation / optimal transport / transfer learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 321, IBISML2021-27, pp. 67-74, Jan. 2022.
Paper # IBISML2021-27 
Date of Issue 2022-01-10 (IBISML) 
ISSN 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 IBISML2021-27

Conference Information
Committee IBISML  
Conference Date 2022-01-17 - 2022-01-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning, etc. 
Paper Information
Registration To IBISML 
Conference Code 2022-01-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Domain Adaptation with Optimal Transport for Extended Variable Space 
Sub Title (in English)  
Keyword(1) heterogeneous domain adaptation  
Keyword(2) optimal transport  
Keyword(3) transfer learning  
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1st Author's Name Toshimitsu Atiake  
1st Author's Affiliation The Institute of Statistical Mathematics (ISM)
2nd Author's Name Hideitsu Hino  
2nd Author's Affiliation The Institute of Statistical Mathematics/RIKEN Center for Advanced Intelligence Project (ISM/RIKEN)
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Speaker Author-1 
Date Time 2022-01-18 14:40:00 
Presentation Time 20 minutes 
Registration for IBISML 
Paper # IBISML2021-27 
Volume (vol) vol.121 
Number (no) no.321 
Page pp.67-74 
#Pages 8 
Date of Issue 2022-01-10 (IBISML) 

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