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| Presentation | 2013-11-12 15:45 [Poster Presentation] Global Solvers for Variational Bayesian Low-rank Subspace Clustering Shinichi Nakajima (Nikon), Akiko Takeda (Univ. of Tokyo), S. Derin Babacan (Google), Masashi Sugiyama (Tokyo Inst. of Tech.), Ichiro Takeuchi (Nagoya Inst. of Tech.)  IBISML2013-37 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Variational Bayesian (VB) learning, known to be a promising approximation method to Bayesian learning, is generally performed with local search. In this paper, we show that the global VB solution for low-rank subspace clustering (LRSC) is relatively easily obtained, and propose its efficient approximation. We first prove that the VB-LRSC is highly redundant, and decompose the optimization problem into small subproblems, each of which has a small number of unknown variables. Since the stationary condition of each subproblem consists of a set of polynomial equations, the global solution is attainable with the homotopy method. For further computational efficiency, we also propose an efficient approximate variant, of which the stationary condition can be written as a polynomial equation with a single variable. We experimentally show the usefulness of our approach. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | subspace clustering / low-rank / variational Bayes / global solution / homotopy method / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 113, no. 286, IBISML2013-37, pp. 7-14, Nov. 2013. | |
| Paper # | IBISML2013-37 | |
| Date of Issue | 2013-11-05 (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 | IBISML2013-37 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2013-11-10 - 2013-11-13 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tokyo Institute of Technology, Kuramae-Kaikan |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | The 16th IBIS Workshop & The 2nd IBIS Tutorial |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2013-11-IBISML |
| Language | English (Japanese title is available) |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Global Solvers for Variational Bayesian Low-rank Subspace Clustering |
| Sub Title (in English) | |
| Keyword(1) | subspace clustering |
| Keyword(2) | low-rank |
| Keyword(3) | variational Bayes |
| Keyword(4) | global solution |
| Keyword(5) | homotopy method |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Shinichi Nakajima |
| 1st Author's Affiliation | Nikon Corporation (Nikon) |
| 2nd Author's Name | Akiko Takeda |
| 2nd Author's Affiliation | The University of Tokyo (Univ. of Tokyo) |
| 3rd Author's Name | S. Derin Babacan |
| 3rd Author's Affiliation | Google Inc. (Google) |
| 4th Author's Name | Masashi Sugiyama |
| 4th Author's Affiliation | Tokyo Institute of Technology (Tokyo Inst. of Tech.) |
| 5th Author's Name | Ichiro Takeuchi |
| 5th Author's Affiliation | Nagoya Institute of Technology (Nagoya Inst. of Tech.) |
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| Speaker | Author-1 |
| Date Time | 2013-11-12 15:45:00 |
| Presentation Time | 180 minutes |
| Registration for | IBISML |
| Paper # | IBISML2013-37 |
| Volume (vol) | vol.113 |
| Number (no) | no.286 |
| Page | pp.7-14 |
| #Pages | 8 |
| Date of Issue | 2013-11-05 (IBISML) |
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