Feature similarity: geometrical framework and discriminative kernel learning

Material type: 文書・図像類

Author: Nguyen, Canh Haoほか

Publisher: 北陸先端科学技術大学院大学知識科学研究科

Publication date: 2009-02-20

Material Format: Digital

Capacity, size, etc.: -

NDC: -

View All

Notes on use

Note (General)：: We propose geometrical models of features of a learning problem. We show that there are two alternative ways to interpret similarities between feature...

Search by Bookstore

Holdings of Libraries in Japan

This page shows libraries in Japan other than the National Diet Library that hold the material.

List of Cooperating Institutions and Databases

Please contact your local library for information on how to use materials or whether it is possible to request materials from the holding libraries.

other

JAIST Repository
Digital
You can check the holdings of institutions and databases with which 学術機関リポジトリデータベース（IRDB）（機関リポジトリ） is linked at the site of 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）.
JAIST Repository

Search by Bookstore

Bibliographic Record

You can check the details of this material, its authority (keywords that refer to materials on the same subject, author's name, etc.), etc.

Digital

Material Type: 文書・図像類
Title: Feature similarity: geometrical framework and discriminative kernel learning
Author/Editor: Nguyen, Canh Hao
Ho, Tu Bao
Author Heading: Nguyen, Canh Hao
Ho, Tu Bao
Publication, Distribution, etc.: 北陸先端科学技術大学院大学知識科学研究科
Publication Date: 2009-02-20
Publication Date (W3CDTF): 2009-02-20
Periodical title: Research report (School of Knowledge Science, Japan Advanced Institute of Science and Technology)
No. or year of volume/issue: KS-RR-2009-001
Volume: KS-RR-2009-001
Pages: 1-22
ISSN (Periodical Title): ISSN : 1347-1570
Text Language Code: eng
Target Audience: 一般
Note (General): We propose geometrical models of features of a learning problem. We show that there are two alternative ways to interpret similarities between features. The syntactic way is well studied and applied in feature selection while the semantic way, in the context of kernel methods, is less understood. We show that the latter is equivalent to feature semantic similarity (FSS) and there are a number of methods that fall into this framework. We analyze to show relations among these methods and differences to feature selection ones. Our analysis shows a natural extension to all these methods. It automatically suggests that this framework can be applied in a general context. We also note that all the methods using FSS are inherently unsupervised in nature. None of these methods make use of labels for classification or regression tasks. On the other hand, the feature selection counterparts consider labels as an important information. Therefore, we propose an algorithm to learn the feature proximity matrix for FSS for supervised tasks. We show the merit of our algorithm in various applications.
リサーチレポート（北陸先端科学技術大学院大学知識科学研究科）
identifier:https://dspace.jaist.ac.jp/dspace/handle/10119/8448
Format (IMT): application/pdf
Source: https://dspace.jaist.ac.jp/dspace/bitstream/10119/8448/1/KS-RR-2009-001.pdf
https://dspace.jaist.ac.jp/dspace/bitstream/10119/8448/1/KS-RR-2009-001.pdf
Access Restrictions: 限定公開
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 北陸先端科学技術大学院大学 : JAIST学術研究成果リポジトリ