IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
... (for ESS/CS/ES/ISS)
Tech. Rep. Archives
... (for ES/CS)
 Go Top Page Go Previous   [Japanese] / [English] 

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)
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

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  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
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)
3rd Author's Name  
3rd Author's Affiliation ()
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker
Date Time 2017-03-01 12:40:00 
Presentation Time 90 
Registration for SIP 
Paper # IEICE-EA2016-102,IEICE-SIP2016-157,IEICE-SP2016-97 
Volume (vol) IEICE-116 
Number (no) no.475(EA), no.476(SIP), no.477(SP) 
Page pp.115-118 
#Pages IEICE-4 
Date of Issue IEICE-EA-2017-02-22,IEICE-SIP-2017-02-22,IEICE-SP-2017-02-22 


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan