Paper Abstract and Keywords |
Presentation |
2022-08-26 15:33
Locally-Structured Unitary Network to Capture Tangent Spaces of Manifold Godage Yasas, Shogo Muramatsu (Niigata Univ.) SIP2022-75 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
This work proposes a unique linear transform, locally-structured unitary network (LSUN), that captures tangent spaces of a manifold latent in data, enabling systematic and highly interpretable data-driven dimensionality reduction. LSUN introduces shift variability to the system with locally and adaptively controllable linear layer under the structural constraint of global unitarity. It provides shift variability that convolution does not, with the same overlap and locality as convolution. The proposed method can be an alternative method for realizing manifold learning. Existing filter-bank based transforms lack the shift-variability because they use fixed filter kernels in a convolutional structure. Local selection of filter kernels, such as sparse modeling, can capture tangent spaces, but the filter kernels obtained through training are redundant and the filters themselves are not very interpretable. To solve these problems, we propose a method to locally control coordinate axes by combining linear transforms such as rotation, shift, and butterfly layers that preserve unitarity, inspired by the multilayer structure of the non-separable oversampled
lapped transform (NSOLT). The significance of the proposed LSUN is verified through low-dimensional approximation and noise reduction experiments. As well, it is also shown that LSUN can be an approach for capturing tangent spaces. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Tangent space learning / shift variability / unitarity / linear transforms / self-supervised learning / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 165, SIP2022-75, pp. 129-133, Aug. 2022. |
Paper # |
SIP2022-75 |
Date of Issue |
2022-08-18 (SIP) |
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) |
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SIP2022-75 |
Conference Information |
Committee |
SIP |
Conference Date |
2022-08-25 - 2022-08-26 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Nobumoto Ohama Memorial Hall (Ishigaki Island) |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Signal processing, etc. |
Paper Information |
Registration To |
SIP |
Conference Code |
2022-08-SIP |
Language |
English |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Locally-Structured Unitary Network to Capture Tangent Spaces of Manifold |
Sub Title (in English) |
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Keyword(1) |
Tangent space learning |
Keyword(2) |
shift variability |
Keyword(3) |
unitarity |
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linear transforms |
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self-supervised learning |
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1st Author's Name |
Godage Yasas |
1st Author's Affiliation |
Niigata University (Niigata Univ.) |
2nd Author's Name |
Shogo Muramatsu |
2nd Author's Affiliation |
Niigata University (Niigata Univ.) |
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Speaker |
Author-1 |
Date Time |
2022-08-26 15:33:00 |
Presentation Time |
18 minutes |
Registration for |
SIP |
Paper # |
SIP2022-75 |
Volume (vol) |
vol.122 |
Number (no) |
no.165 |
Page |
pp.129-133 |
#Pages |
5 |
Date of Issue |
2022-08-18 (SIP) |
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