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Feature Selection For High-Dimensional Clustering
lib:484494890eaf7043 (v1.0.0)
Authors: Larry Wasserman,Martin Azizyan,Aarti Singh
ArXiv: 1406.2240
Document: PDF DOI
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.
ArXiv: 1406.2240
Document: PDF DOI
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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