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Feature Selection For High-Dimensional Clustering

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Authors: Larry Wasserman,Martin Azizyan,Aarti Singh
ArXiv: 1406.2240
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Abstract URL: http://arxiv.org/abs/1406.2240v1


We present a nonparametric method for selecting informative features in high-dimensional clustering problems. We start with a screening step that uses a test for multimodality. Then we apply kernel density estimation and mode clustering to the selected features. The output of the method consists of a list of relevant features, and cluster assignments. We provide explicit bounds on the error rate of the resulting clustering. In addition, we provide the first error bounds on mode based clustering.

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