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A Novel Stability based Feature Selection Framework for k-means Clustering
Published on Oct 03, 20113280 Views
Stability of a learning algorithm with respect to small input perturbations is an important property, as it implies the derived models to be robust with respect to the presence of noisy features and
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A Novel Stability based Feature Selection Framework for k-means Clustering00:00
Presentation outline00:22
What's new00:51
k-means and PCA01:55
Feature selection and Sparse PCA03:23
Stability of PCA04:35
Stability maximizing sparse PCA05:37
Algorithm10:40
Deflation for multiple eigenvectors11:26
Empirical results11:46
Clustering (1)12:54
Clustering (2)13:56
Clustering (3)14:03
Clustering (4)14:09
Qualitative evaluation14:11
Further work15:35
Algorithmic approach16:41