A flexible and effective linearization method for subspace learning

Feiping Nie; Dong Xu; Ivor W. Tsang; Changshui Zhang

doi:10.1007/978-1-4614-4457-2_8

A flexible and effective linearization method for subspace learning

Feiping Nie
, Dong Xu
, Ivor W. Tsang
, Changshui Zhang

University of Texas at Arlington
Nanyang Technological University
Tsinghua University

科研成果: 书/报告/会议事项章节 › 章节 › 同行评审

3 引用（Scopus）

摘要

In the past decades, a large number of subspace learning or dimension reduction methods [2,16,20,32,34,37,44] have been proposed. Principal component analysis (PCA) [32] pursues the directions of maximum variance for optimal reconstruction. Linear discriminant analysis (LDA) [2], as a supervised algorithm, aims to maximize the inter-class scatter and at the same timeminimize the intra-class scatter. Due to utilization of label information, LDA is experimentally reported to outperform PCA for face recognition, when sufficient labeled face images are provided [2].

源语言	英语
主期刊名	Graph Embedding for Pattern Analysis
出版商	Springer New York
页	177-203
页数	27
ISBN（电子版）	9781461444572
ISBN（印刷版）	9781461444565
DOI	https://doi.org/10.1007/978-1-4614-4457-2_8
出版状态	已出版 - 1 1月 2013
已对外发布	是

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AU - Xu, Dong

AU - Tsang, Ivor W.

AU - Zhang, Changshui

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AB - In the past decades, a large number of subspace learning or dimension reduction methods [2,16,20,32,34,37,44] have been proposed. Principal component analysis (PCA) [32] pursues the directions of maximum variance for optimal reconstruction. Linear discriminant analysis (LDA) [2], as a supervised algorithm, aims to maximize the inter-class scatter and at the same timeminimize the intra-class scatter. Due to utilization of label information, LDA is experimentally reported to outperform PCA for face recognition, when sufficient labeled face images are provided [2].

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PB - Springer New York

ER -

A flexible and effective linearization method for subspace learning

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