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2008

SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis

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SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
Linear Discriminant Analysis (LDA) has been a popular method for extracting features that preserves class separability. The projection functions of LDA are commonly obtained by maximizing the between-class covariance and simultaneously minimizing the within-class covariance. It has been widely used in many fields of information processing, such as machine learning, data mining, information retrieval, and pattern recognition. However, the computation of LDA involves dense matrices eigendecomposition, which can be computationally expensive in both time and memory. Specifically, LDA has O
Deng Cai, Xiaofei He, Jiawei Han
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TKDE
Authors Deng Cai, Xiaofei He, Jiawei Han
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