Title :
Clustering via kernel decomposition
Author :
Szymkowiak-Have, A. ; Girolami, Mark A. ; Larsen, Jan
Author_Institution :
Informatics & Math. Modeling, Tech. Univ. of Denmark, Lyngby, Denmark
Abstract :
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.
Keywords :
Markov processes; eigenvalues and eigenfunctions; principal component analysis; affinity matrix; eigenvalue decomposition; kernel decomposition; kernel principal component analysis; nonparametric density estimator; posterior probabilities; spectral clustering methods; standard cross-validation methods; Clustering methods; Data mining; Eigenvalues and eigenfunctions; Feature extraction; Informatics; Kernel; Mathematical model; Matrix decomposition; Parameter estimation; Principal component analysis; Aggregated Markov model; kernel decomposition; kernel principal component analysis (KPCA); spectral clustering;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2005.860840
