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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
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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) Computationally Efficient Estimation of Squared-loss Mutual Information with Multiplicative Kernel Models 
Sub Title (in English)  
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  
Keyword(6)  
Keyword(7)  
Keyword(8)  
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
Date of Issue 2013-11-05 (IBISML) 


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