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| Presentation | 2023-03-01 15:50 Multiscale Manifold Clustering and Embedding with Multiple Kernels Kyohei Suzuki, Masahiro Yukawa (Keio Univ.)  EA2022-123 SIP2022-167 SP2022-87 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This paper presents a clustering and embedding method to analyze data which lie on a union of multiple manifolds having different scales. The proposed method executes the following two processes alternately: (i) data are clustered by using bases of the manifolds, and (ii) the bases are updated based on the clustered data. To achieve this, the task is cast as maximization of the variance of data mapped to a subspace of the reproducing kernel Hilbert space that corresponds to the relevant manifold of each cluster. The optimization problem involves real-valued variables to construct the bases and binary variables to assign each datum to its corresponding cluster. Each set of variables is updated in an alternate fashion. For sake of scalability, we extend the domain of the binary variables to the whole Euclidean space under normalization subject to the orthonormality constraint. Despite the nonconvexity of the constraint, the problem in terms of each set of variables admits an analytic solution. The efficacy of the proposed algorithm is demonstrated by simulations using toy data. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | manifold clustering / manifold learning / multiscale framework / kernel methods / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 388, SIP2022-167, pp. 276-281, Feb. 2023. | |
| Paper # | SIP2022-167 | |
| Date of Issue | 2023-02-21 (EA, SIP, SP) | |
| 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 | EA2022-123 SIP2022-167 SP2022-87 |
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| Conference Information | |
| Committee | SP IPSJ-SLP EA SIP |
| Conference Date | 2023-02-28 - 2023-03-01 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | SIP |
| Conference Code | 2023-02-SP-SLP-EA-SIP |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Multiscale Manifold Clustering and Embedding with Multiple Kernels |
| Sub Title (in English) | |
| Keyword(1) | manifold clustering |
| Keyword(2) | manifold learning |
| Keyword(3) | multiscale framework |
| Keyword(4) | kernel methods |
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| 1st Author's Name | Kyohei Suzuki |
| 1st Author's Affiliation | Keio University (Keio Univ.) |
| 2nd Author's Name | Masahiro Yukawa |
| 2nd Author's Affiliation | Keio University (Keio Univ.) |
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| Speaker | Author-1 |
| Date Time | 2023-03-01 15:50:00 |
| Presentation Time | 20 minutes |
| Registration for | SIP |
| Paper # | EA2022-123, SIP2022-167, SP2022-87 |
| Volume (vol) | vol.122 |
| Number (no) | no.387(EA), no.388(SIP), no.389(SP) |
| Page | pp.276-281 |
| #Pages | 6 |
| Date of Issue | 2023-02-21 (EA, SIP, SP) |
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