Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data

This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density estimate. We show how to convert the ensemble estimates into a Mercer Kernel, describe the properties of this new kernel function, and give examples of the performance of this kernel on unsupervised clustering of synthetic data and also in the domain of unsupervised multispectral image understanding.

• Type: Conference
• Year: 2004
• Where: SDM
• Authors: Ashok N. Srivastava
• Keywords: Data Mining, Mercer Kernel, Mixture Model, Probabilistic Mixture Models, SDM 2004