Adaptive Dimension Reduction Using Discriminant Analysis and K -means Clustering
Tao Li - Florida International University, USA
Chris Ding - Lawrence Berkeley National Lab, USA
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. Relations among PCA, LDA, K -means are clarified. Extensive experimental results on real-world datasets show the effectiveness of our approach.