Discriminative cluster analysis

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Discriminative cluster analysis
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 rela...
Fernando De la Torre, Takeo Kanade
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2006
Where ICML
Authors Fernando De la Torre, Takeo Kanade
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