Informational Energy Kernel for LVQ

We describe a kernel method which uses the maximization of Onicescu’s informational energy as a criteria for computing the relevances of input features. This adaptive relevance determination is used in combination with the neural-gas and the generalized relevance LVQ algorithms. Our quadratic optimization function, as an L2 type method, leads to linear gradient and thus easier computation. We obtain an approximation formula similar to the mutual information based method, but in a more simple way.

• Type: Conference
• Year: 2005
• Where: ICANN
• Authors: Angel Cataron, Razvan Andonie
• Keywords: Adaptive Relevance Determination, ICANN 2005, Onicescu’s Informational Energy, Relevance Lvq Algorithms