NDLI: Linear Representation Learning Using Sphere Factor Analysis

Content Provider	IEEE Xplore Digital Library
Author	Yiming Wu Xiuwen Liu Mio, W.
Copyright Year	2009
Abstract	Representation learning is a fundamental challenge for feature selection and plays an important role in applications such as dimension reduction, data mining and object recognition. Traditional linear representation methods, such as principal component analysis (PCA), independent component analysis (ICA) and linear discriminate analysis (LDA), have good performance on certain applications based on corresponding criteria. However, these linear representation methods are not optimal in general. Sphere factor analysis (SFA) is a recently proposed method which provides a general framework for optimization problems. In term of object recognition, SFA seeks to optimize the discriminant ability of the nearest neighbor classifier for data classification and labeling. Based on the geometry structure of the search space, a gradient search algorithms have been applied to obtain an optimal basis. A detail presentation of these algorithm is given in this paper. Furthermore, to speed up the search procedure of SFA, a two-stage strategy is proposed, which we called two-stage SFA. We illustrate the effectiveness of the original SFA and two-stage SFA methods on UCI data sets and two face data sets.
Starting Page	12
Ending Page	17
File Size	201575
Page Count	6
File Format	PDF
ISBN	9780769539263
DOI	10.1109/ICMLA.2009.127
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sphere Factor Analysis Optimization methods Independent component analysis Object recognition Data mining Optimal Basis Search Face Recognition Nearest neighbor searches Geometry Performance analysis Linear discriminant analysis Labeling Linear Representation Principal component analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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