Discriminative Cluster Analysis

Abstract

Clustering is one of the most widely used statistical tools for data analysis. Among all existing clustering techniques, k-means is a very popular method because of its ease of programming and because it accomplishes a good trade-off between achieved performance and computational complexity. However, kmeans is prone to local minima problems, and it does not scale well with high dimensional data sets. A common approach to dealing with high dimensional data is to cluster in the space spanned by the principal components (PC). In this paper, we show the benefits of clustering in a low dimensional discriminative space rather than in the PC space (generative). In particular, we propose a new clustering algorithm called Discriminative Cluster Analysis (DCA). DCA jointly performs dimensionality reduction and clustering. Several toy and real examples show the benefits of DCA versus traditional PCA+k-means clustering. Additionally, a new matrix formulation is suggested and connections with related techniques such as spectral graph methods and linear discriminant analysis are provided.