Optimality and stability of the K-hyperline clustering algorithm
Abstract
K-hyperline clustering is an iterative algorithm based on singular value decomposition and it has been successfully used in sparse component analysis. In this paper, we prove that the algorithm converges to a locally optimal solution for a given set of training data, based on Lloyd’s optimality conditions. Furthermore, the local optimality is shown by developing an Expectation-Maximization procedure for learning dictionaries to be used in sparse...
Paper Details
Title
Optimality and stability of the K-hyperline clustering algorithm
Published Date
Jul 1, 2011
Journal
Volume
32
Issue
9
Pages
1299 - 1304
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