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Paper Abstract and Keywords
Presentation 2016-07-06 14:55
Classification analysis of high-dimensional data based on L0-norm optimization.
Noriki Ito, Masashi Sato (UEC Tokyo), Yoshiyuki Kabashima (Tokyo Tech), Yoichi Miyawaki (UEC Tokyo) NC2016-14
Abstract (in Japanese) (See Japanese page) 
(in English) Advances in sensing devices allow us to measure high-dimensional data easily, but the sample size is often limited because of various reasons such as costs and duration to perform experiments. In such circumstances, feature selection plays a vital role to establish reliable models to describe characteristics of the high-dimensional data. For this purpose, we study iterative algorithms for L0-norm optimization that controls a number of features to be selected. The algorithms have been actively developed for compressed sensing, but not for classification problems explicitly. In this paper, we formulated a classification model with L0-norm regularization based on iterative hard thresholding (IHT) algorithm, quantified its performance in terms of accuracy in classification and feature selection, and compared the performance with that of representative models of a non-sparse classifier (support vector machine) and a sparse classifier (sparse logistic regression). Results showed that the IHT-based classifier outperformed the non-sparse classifier in terms of classification accuracy and did a sparse classifier in terms of feature selection accuracy for certain noise conditions. These results suggest that the proposed model serves an effective means to extract important features embedded in the high-dimensional data.
Keyword (in Japanese) (See Japanese page) 
(in English) feature selection / L0-norm optimization / iterative hard thresholding / high-dimensional data / sparse modeling / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 120, NC2016-14, pp. 223-228, July 2016.
Paper # NC2016-14 
Date of Issue 2016-06-28 (NC) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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)
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Conference Information
Conference Date 2016-07-04 - 2016-07-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning Approach to Biodata Mining, and General 
Paper Information
Registration To NC 
Conference Code 2016-07-NC-BIO-IBISML-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Classification analysis of high-dimensional data based on L0-norm optimization. 
Sub Title (in English)  
Keyword(1) feature selection  
Keyword(2) L0-norm optimization  
Keyword(3) iterative hard thresholding  
Keyword(4) high-dimensional data  
Keyword(5) sparse modeling  
1st Author's Name Noriki Ito  
1st Author's Affiliation The University of Electro-Communications (UEC Tokyo)
2nd Author's Name Masashi Sato  
2nd Author's Affiliation The University of Electro-Communications (UEC Tokyo)
3rd Author's Name Yoshiyuki Kabashima  
3rd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
4th Author's Name Yoichi Miyawaki  
4th Author's Affiliation The University of Electro-Communications (UEC Tokyo)
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Date Time 2016-07-06 14:55:00 
Presentation Time 25 
Registration for NC 
Paper # IEICE-NC2016-14 
Volume (vol) IEICE-116 
Number (no) no.120 
Page pp.223-228 
#Pages IEICE-6 
Date of Issue IEICE-NC-2016-06-28 

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