ITE Technical Group Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2018-02-16 11:30
Accuracy Improvement of Preference Estimation for Video Using SFEM-GS
Yoshiki Ito, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we present two kinds of canonical correlation analysis methods, supervised fractional-order embedding multiview canonical correlation analysis (SFEMCCA) and its geometrical version, SFEMCCA with geometrical structure (SFEM-GS). They are CCA methods realizing the following three points: (1) learning from noisy data with small number of samples and large number of dimensions, (2) multiview learning that can integrate three or more kinds of features, and (3) supervised learning using labels corresponding to the samples. In real world, there are many cases requiring the above three learning techniques. Since our previous researches also are able to adopt these learning techniques, these CCA methods, which takes them into account, are effective for our previous researches. Experimental results indicated that estimation accuracies using our methods were statistically significant (p < 0.01) compared to those of several conventional methods of supervised CCA.
Keyword (in Japanese) (See Japanese page) 
(in English) canonical correlation analysis / fractional-order technique / local structure preservation / discriminant analysis / feature extraction / / /  
Reference Info. ITE Tech. Rep., vol. 42, no. 4, ME2018-34, pp. 315-318, Feb. 2018.
Paper # ME2018-34 
Date of Issue 2018-02-08 (MMS, HI, ME, AIT) 
ISSN Print edition: ISSN 1342-6893    Online edition: ISSN 2424-1970
Download PDF

Conference Information
Committee IEICE-ITS IEICE-IE MMS HI ME AIT  
Conference Date 2018-02-15 - 2018-02-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To ME 
Conference Code 2018-02-ITS-IE-MMS-HI-ME-AIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Accuracy Improvement of Preference Estimation for Video Using SFEM-GS 
Sub Title (in English)  
Keyword(1) canonical correlation analysis  
Keyword(2) fractional-order technique  
Keyword(3) local structure preservation  
Keyword(4) discriminant analysis  
Keyword(5) feature extraction  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Yoshiki Ito  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Takahiro Ogawa  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Miki Haseyama  
3rd Author's Affiliation Hokkaido University (Hokkaido 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 ()
Speaker Author-1 
Date Time 2018-02-16 11:30:00 
Presentation Time 15 minutes 
Registration for ME 
Paper # MMS2018-34, HI2018-34, ME2018-34, AIT2018-34 
Volume (vol) vol.42 
Number (no) no.4 
Page pp.315-318 
#Pages
Date of Issue 2018-02-08 (MMS, HI, ME, AIT) 


[Return to Top Page]

[Return to ITE Web Page]


The Institute of Image Information and Television Engineers (ITE), Japan