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| Presentation | 2013-03-05 16:30 Similarity adaptation for label propagation based on local linear reconstruction Masayuki Karasuyama, Hiroshi Mamitsuka (Kyoto Univ.)  IBISML2012-109 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Label propagation is one of the state-of-the-art methods for semi-supervised learning, which estimates labels by propagating label information through a graph. Given input data, i.e. feature vectors, label propagation typically proceeds with the following three steps: 1) generating graph edges, 2) estimating edge weights and 3) propagating label information. This paper focuses on the second step; estimating edge weights for subsequent propagation step. Our approach uses a local linear reconstruction with a constraint that edges are parameterized by a similarity function of node pairs. As a result, our generated graph captures the manifold structure of the input data, where each edge represents similarity of each node pair at the same time. For further justification, we provide analytical considerations including an error analysis based on a low dimensional manifold model. Experimental results demonstrated the effectiveness of our approach both in synthetic and real datasets. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Graph-based semi-supervised learning / label propagation / graph edge weight estimation / local linear approximation / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 112, no. 454, IBISML2012-109, pp. 115-121, March 2013. | |
| Paper # | IBISML2012-109 | |
| Date of Issue | 2013-02-25 (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 | IBISML2012-109 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2013-03-04 - 2013-03-05 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nagoya Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2013-03-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Similarity adaptation for label propagation based on local linear reconstruction |
| Sub Title (in English) | |
| Keyword(1) | Graph-based semi-supervised learning |
| Keyword(2) | label propagation |
| Keyword(3) | graph edge weight estimation |
| Keyword(4) | local linear approximation |
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| 1st Author's Name | Masayuki Karasuyama |
| 1st Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 2nd Author's Name | Hiroshi Mamitsuka |
| 2nd Author's Affiliation | Kyoto University (Kyoto Univ.) |
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| Speaker | Author-1 |
| Date Time | 2013-03-05 16:30:00 |
| Presentation Time | 25 minutes |
| Registration for | IBISML |
| Paper # | IBISML2012-109 |
| Volume (vol) | vol.112 |
| Number (no) | no.454 |
| Page | pp.115-121 |
| #Pages | 7 |
| Date of Issue | 2013-02-25 (IBISML) |
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