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Paper Abstract and Keywords
Presentation 2011-06-20 11:05
SERAPH: Semi-supervised Metric Learning Paradigm with Hyper Sparsity
Gang Niu (Tokyo Inst. of Tech.), Bo Dai (Chinese Academy Of Sciences), Makoto Yamada, Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2011-8
Abstract (in Japanese) (See Japanese page) 
(in English) We consider the problem of learning a distance metric from very limited side information with unlabeled data. The proposed SERAPH (SEmi-supervised metRic leArning Paradigm with Hyper sparsity) is a direct and substantially more natural approach for semi-supervised metric learning, since the supervised and unsupervised parts are based on a unified information-theoretic framework. Unlike other extensions, the unsupervised part of SERAPH can extract further side information from unlabeled data according to temporary results of the supervised part, and thus interacts with the supervised part positively. SERAPH involves both the sparsity of posterior distributions over unobserved weak labels and the sparsity of induced projection matrices, which we call the hyper sparsity. The resulting optimization is solved by an EM-like scheme, where M-Step is convex, and E-Step has analytical solution. Experimental results show that SERAPH compares favorably with existing metric learning algorithms based on weak labels.
Keyword (in Japanese) (See Japanese page) 
(in English) Metric Learning / Posterior Sparsity / Projection Sparsity / / / / /  
Reference Info. IEICE Tech. Rep., vol. 111, no. 87, IBISML2011-8, pp. 51-58, June 2011.
Paper # IBISML2011-8 
Date of Issue 2011-06-13 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee IBISML  
Conference Date 2011-06-20 - 2011-06-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Takeda Hall 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine learning and its applications 
Paper Information
Registration To IBISML 
Conference Code 2011-06-IBISML 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) SERAPH: Semi-supervised Metric Learning Paradigm with Hyper Sparsity 
Sub Title (in English)  
Keyword(1) Metric Learning  
Keyword(2) Posterior Sparsity  
Keyword(3) Projection Sparsity  
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1st Author's Name Gang Niu  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Inst. of Tech.)
2nd Author's Name Bo Dai  
2nd Author's Affiliation Chinese Academy Of Sciences (Chinese Academy Of Sciences)
3rd Author's Name Makoto Yamada  
3rd Author's Affiliation Tokyo Institute of Technology (Tokyo Inst. of Tech.)
4th Author's Name Masashi Sugiyama  
4th Author's Affiliation Tokyo Institute of Technology (Tokyo Inst. of Tech.)
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Speaker Author-1 
Date Time 2011-06-20 11:05:00 
Presentation Time 30 minutes 
Registration for IBISML 
Paper # IBISML2011-8 
Volume (vol) vol.111 
Number (no) no.87 
Page pp.51-58 
#Pages 8 
Date of Issue 2011-06-13 (IBISML) 

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