NDLI: A unified framework for generalized Linear Discriminant Analysis

Content Provider	IEEE Xplore Digital Library
Author	Shuiwang Ji Jieping Ye
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Arizona State Univ., Tempe, AZ (Shuiwang Ji; Jieping Ye)
Abstract	Linear discriminant analysis (LDA) is one of the well- known methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope with the curse of dimensionality. In essence, most of these algorithms employ various techniques to deal with the singularity problem, which occurs when the data dimensionality is larger than the sample size. They have been applied successfully in various applications. However, there is a lack of a systematic study of the commonalities and differences of these algorithms, as well as their intrinsic relationships. In this paper, a unified framework for generalized LDA is proposed via a transfer function. The proposed framework elucidates the properties of various algorithms and their relationships. Based on the presented analysis, we propose an efficient model selection algorithm for LDA. We conduct extensive experiments using a collection of high-dimensional data, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms.
Starting Page	1
Ending Page	7
File Size	225738
Page Count	7
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587377
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear discriminant analysis Gene expression Principal component analysis Transfer functions Data analysis Scattering Data mining Text categorization Face recognition Computer science
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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