Clustering via Kernel Decomposition
Anna Szymkowiak-Have, Mark Girolami and Jan Larsen
IEEE Transactions on Neural Networks
Spectral clustering methods were proposed recently
which rely on the eigenvalue decomposition of an affinity matrix. In this work the affinity matrix is created from the elements of a non-parametric density estimator and then decomposed to obtain posterior probabilities of class membership. Hyperparameters are selected using standard cross-validation methods.