Clustering via kernel decomposition

• Authors: A. Szymkowiak-Have;M. A. Girolami;J. Larsen
• Venue: IEEE Transactions on Neural Networks
• Year: 2006

Spectral clustering methods were proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this letter, the affinity matrix is created from the elements of a nonparametric density estimator and then decomposed to obtain posterior probabilities of class membership. Hyperparameters are selected using standard cross-validation methods.