Paper Abstract and Keywords |
Presentation |
2013-11-13 15:45
[Poster Presentation]
Computationally Efficient Estimation of Squared-loss Mutual Information with Multiplicative Kernel Models Tomoya Sakai, Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2013-53 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
emph{Squared-loss mutual information} (SMI) is a robust measure of statistical dependence between random variables.
The sample-based SMI approximatorcalled emph{least-squares mutual information} (LSMI) was demonstrated to be useful in solving various machine learning tasks such as dimension reduction, clustering, and causal inference. The original LSMI approximates the pointwise mutual information
using the kernel model, which is a linear combination of kernel basis functions located on emph{paired} data samples. Although LSMI was proved to achieve the optimal approximation accuracy in the limit of large sample size, its approximation capability is limited when the sample size is small due to lack of kernel basis functions. Increasing the number of kernel basis functions can mitigate this weakness, but a naive implementation of this idea significantly increases the computation costs.
In this paper, we show that the computational complexity of LSMI with the emph{multiplicative} kernel model, which locates kernel basis functions on emph{unpaired} data samples and thus the number of kernel basis functions is the sample size squared, is the same as that for the plain kernel model. We experimentally demonstrate that LSMI with the multiplicative kernel model is more accurate than that with plain kernel models in small sample cases, with only mild increase in computation time. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
squared-loss mutual information / least-squares mutual information / density ratio estimation / multiplicative kernel models / independence test / / / |
Reference Info. |
IEICE Tech. Rep., vol. 113, no. 286, IBISML2013-53, pp. 131-137, Nov. 2013. |
Paper # |
IBISML2013-53 |
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) |
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IBISML2013-53 |
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) |
Computationally Efficient Estimation of Squared-loss Mutual Information with Multiplicative Kernel Models |
Sub Title (in English) |
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Keyword(1) |
squared-loss mutual information |
Keyword(2) |
least-squares mutual information |
Keyword(3) |
density ratio estimation |
Keyword(4) |
multiplicative kernel models |
Keyword(5) |
independence test |
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1st Author's Name |
Tomoya Sakai |
1st Author's Affiliation |
Tokyo Institute of Technology (Tokyo Inst. of Tech.) |
2nd Author's Name |
Masashi Sugiyama |
2nd Author's Affiliation |
Tokyo Institute of Technology (Tokyo Inst. of Tech.) |
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Speaker |
Author-1 |
Date Time |
2013-11-13 15:45:00 |
Presentation Time |
180 minutes |
Registration for |
IBISML |
Paper # |
IBISML2013-53 |
Volume (vol) |
vol.113 |
Number (no) |
no.286 |
Page |
pp.131-137 |
#Pages |
7 |
Date of Issue |
2013-11-05 (IBISML) |
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