Projection-Based Approximation and a Duality with Kernel Methods

Knowledge-Based Kernel Approximation

Prior knowledge, in the form of linear inequalities that need to be satisfied over multiple polyhedral sets, is incorporated into a function approximation generated by a linear combination of linear or nonlinear kernels. In addition, the approximation needs to satisfy conventional conditions such as having given exact or inexact function values at certain points. Determining such an approximati...

Training set approximation for kernel methods

We propose a technique for a training set approximation and its usage in kernel methods. The approach aims to represent data in a low dimensional space with possibly minimal representation error which is similar to the Principal Component Analysis (PCA). In contrast to the PCA, the basis vectors of the low dimensional space used for data representation are properly selected vectors from the tra...

Kernel Approximation Methods for Speech Recognition

Kernel Approximation Methods for Speech Recognition

Kernel-based Clustering and Low Rank Approximation

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Kernel Based Meshless Methods