Approximate kernel competitive learning
Kernel competitive learning (KCL) cannot be applied in large scale data problem. Propose a projection based approximate KCL method for large scale data problem. Provide theoretical analysis on why the approximation modelling would work for KCL. A pseudo-parallelled approximate computation framework for large scale KCL is developed. Experimentally show the effectiveness and efficiency of the proposals.
kernel competitive learning kernel clustering large scale computation