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Discriminative Cluster Analysis
F. De la Torre Frade and T. Kanade
International Conference on Machine Learning, ACM Press, New York, NY, USA, Vol. 148, June, 2006, pp. 241 - 248.

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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, k-means is prone to local minima problems, and it does not scale too 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 proposed and connections with related techniques such as spectral graph methods and linear discriminant analysis are provided.

Associated center: VASC
Associated project: Component Analysis for Data Analysis

Number of pages: 8

F. De la Torre Frade and T. Kanade, "Discriminative Cluster Analysis," International Conference on Machine Learning, ACM Press, New York, NY, USA, Vol. 148, June, 2006, pp. 241 - 248.

@inproceedings{De la Torre Frade_2006_5715,
   author = "Fernando De la Torre Frade and Takeo Kanade",
   title = "Discriminative Cluster Analysis",
   booktitle = "International Conference on Machine Learning",
   month = "June",
   year = "2006",
   volume = "148",
   pages = "241 - 248",
   publisher = "ACM Press",
   address = "New York, NY, USA"
}