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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
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Conference Information
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  
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.)
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Date Time 2018-02-16 11:30:00 
Presentation Time 15 
Registration for ME 
Paper # ITE-MMS2018-34,ITE-HI2018-34,ITE-ME2018-34,ITE-AIT2018-34 
Volume (vol) ITE-42 
Number (no) no.4 
Page pp.315-318 
#Pages ITE-4 
Date of Issue ITE-MMS-2018-02-08,ITE-HI-2018-02-08,ITE-ME-2018-02-08,ITE-AIT-2018-02-08 

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