Paper Abstract and Keywords |
Presentation |
2013-08-29 16:10
[Tutorial Lecture]
Tensor-Based Machine Learning: Modeling, Algorithms and Applications Qibin Zhao, Andrzej Cichocki (RIKEN) SIP2013-73 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Tensors are a generalization of vectors and matrices to higher dimensions that can naturally represent the multidimensional structured data. Based on multilinear algebra, tensor factorizations enable us to effectively capture the hidden structure of the data, which is usually available as a priori information on the data nature. Hence it attracts much interest on unsupervised learning and data exploratory. In this paper, we firstly present some basic concept of tensor factorization and multilinear algebra, then a novel framework for tensor variate Gaussian processes (GP) regression is introduced, which exploits a covariance function defined on tensor representation of data inputs. In this way, we bring together the powerful GP methods supported by Bayesian inference and higher-order tensor analysis techniques into one framework. This enables us to account for the underlying data structure within the model, providing a powerful framework for structural data analysis, such as 3D video sequences. Simulation results on both the synthetic data and a real world application of estimating the crowd size in videos, without the necessarity of the typical segmentations and feature extractions, demonstrate the effectiveness of the proposed approach. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Tensor factorization / multilinear algebra / Gaussian processes / kernel methods / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 113, no. 191, SIP2013-73, pp. 35-40, Aug. 2013. |
Paper # |
SIP2013-73 |
Date of Issue |
2013-08-22 (SIP) |
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) |
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SIP2013-73 |
Conference Information |
Committee |
SIP |
Conference Date |
2013-08-29 - 2013-08-30 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Tokyo University of Agriculture and Technology |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Image processing and related topics |
Paper Information |
Registration To |
SIP |
Conference Code |
2013-08-SIP |
Language |
English |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Tensor-Based Machine Learning: Modeling, Algorithms and Applications |
Sub Title (in English) |
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Tensor factorization |
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multilinear algebra |
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Gaussian processes |
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kernel methods |
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1st Author's Name |
Qibin Zhao |
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RIKEN (RIKEN) |
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Andrzej Cichocki |
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RIKEN (RIKEN) |
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Speaker |
Author-1 |
Date Time |
2013-08-29 16:10:00 |
Presentation Time |
60 minutes |
Registration for |
SIP |
Paper # |
SIP2013-73 |
Volume (vol) |
vol.113 |
Number (no) |
no.191 |
Page |
pp.35-40 |
#Pages |
6 |
Date of Issue |
2013-08-22 (SIP) |
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