Clustering via Kernel Decomposition
A. Szymkowiak-Have, Mark Girolami, Jan Otto Larsen · IEEE Transactions on Neural Networks · 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.