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Paper Abstract and Keywords
Presentation 2017-03-01 12:40
[Poster Presentation] Dual-Sparsification of Kernel Regression Based on Sampling
Atsushi Kojima, Toshihisa Tanaka (TUAT) EA2016-102 SIP2016-157 SP2016-97
Abstract (in Japanese) (See Japanese page) 
(in English) When the input pattern have redundant features in regression analysis or pattern recognition, the prediction accuracy is likely to be lowered. For a kernel regression in a reproducing kernel Hilbert space, as the number of observed input signals increases, the dimension of parameters increases, since a kernel regression model using a kernel method is represented by the linear sum of kernel functions corresponding to input patterns. This can yield overfitting. In this paper, we propose a method for simultaneously selecting features and model coefficients. In order to express a sparsity of the features and the weight coefficients, we generate a binary vector where all the elements is 0 or 1 sampled from the beta process. The proposed method can select effective features and estimate sparse weight coefficients by introducing the binary vector into the kernel regression model. Numerical examples support the efficacy of our proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Reproducing kernel Hilbert space / Sparse kernel regression / Beta process / Feature selection / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 476, SIP2016-157, pp. 115-118, March 2017.
Paper # SIP2016-157 
Date of Issue 2017-02-22 (EA, SIP, SP) 
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)
Download PDF EA2016-102 SIP2016-157 SP2016-97

Conference Information
Committee SP SIP EA  
Conference Date 2017-03-01 - 2017-03-02 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Industry Support Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech, Engineering/Electro Acoustics, Signal Processing, and Related Topics 
Paper Information
Registration To SIP 
Conference Code 2017-03-SP-SIP-EA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Dual-Sparsification of Kernel Regression Based on Sampling 
Sub Title (in English)  
Keyword(1) Reproducing kernel Hilbert space  
Keyword(2) Sparse kernel regression  
Keyword(3) Beta process  
Keyword(4) Feature selection  
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1st Author's Name Atsushi Kojima  
1st Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
2nd Author's Name Toshihisa Tanaka  
2nd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
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Speaker Author-1 
Date Time 2017-03-01 12:40:00 
Presentation Time 90 minutes 
Registration for SIP 
Paper # EA2016-102, SIP2016-157, SP2016-97 
Volume (vol) vol.116 
Number (no) no.475(EA), no.476(SIP), no.477(SP) 
Page pp.115-118 
#Pages 4 
Date of Issue 2017-02-22 (EA, SIP, SP) 

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