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ICML
2007
IEEE

Adaptive dimension reduction using discriminant analysis and K-means clustering

10 years 24 days ago
Adaptive dimension reduction using discriminant analysis and K-means clustering
We combine linear discriminant analysis (LDA) and K-means clustering into a coherent framework to adaptively select the most discriminative subspace. We use K-means clustering to generate class labels and use LDA to do subspace selection. The clustering process is thus integrated with the subspace selection process and the data are then simultaneously clustered while the feature subspaces are selected. We show the rich structure of the general LDA-Km framework by examining its variants and their relationships to earlier approaches. Extensive experimental results on real-world datasets show the effectiveness of our approach.
Chris H. Q. Ding, Tao Li
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Chris H. Q. Ding, Tao Li
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