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 2015-03-19 16:40
Semi-Supervised Low-rank GMM Learning for Multimodal Distribution
Eita Aoki, Tetsuya Matsumoto, Noboru Ohnishi (Nagoya Univ.) BioX2014-60 PRMU2014-180
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we examine the learning method of each category distribution under underdetermined environment that category information of few data are known. Because we use multimodal data distribution, learning for each category distribution is difficult. Therefore, by using the given category information, and projecting the data into low-dimensional subspace suitable for category classification, we propose a method to GMM approximation. Specifically, we consider each category distribution as a linear combination of subclasses of the multidimensional normal distribution, and determine the projection space by appropriate criteria. In the experiment, we compared the proposed method with the conventional method by changing the parameters. In some cases, the proposed method showed higher accuracy than the conventional method.
Keyword (in Japanese) (See Japanese page) 
(in English) Semi-Supervised Learning / Dimensionality Reduction / Multi-Modality / / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 521, PRMU2014-180, pp. 129-134, March 2015.
Paper # PRMU2014-180 
Date of Issue 2015-03-12 (BioX, PRMU) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Download PDF BioX2014-60 PRMU2014-180

Conference Information
Committee PRMU BioX  
Conference Date 2015-03-19 - 2015-03-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2015-03-PRMU-BioX 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Semi-Supervised Low-rank GMM Learning for Multimodal Distribution 
Sub Title (in English)  
Keyword(1) Semi-Supervised Learning  
Keyword(2) Dimensionality Reduction  
Keyword(3) Multi-Modality  
1st Author's Name Eita Aoki  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Tetsuya Matsumoto  
2nd Author's Affiliation Nagoya University (Nagoya Univ.)
3rd Author's Name Noboru Ohnishi  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
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 ()
Date Time 2015-03-19 16:40:00 
Presentation Time 30 
Registration for PRMU 
Paper # IEICE-BioX2014-60,IEICE-PRMU2014-180 
Volume (vol) IEICE-114 
Number (no) no.520(BioX), no.521(PRMU) 
Page pp.129-134 
#Pages IEICE-6 
Date of Issue IEICE-BioX-2015-03-12,IEICE-PRMU-2015-03-12 

[Return to Top Page]

[Return to IEICE Web Page]

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