Patent ID: 6944602

Claim:
A method for training a kernel-based learning machine using a dataset comprising: filling a kernel matrix with a plurality of kernels, each kernel comprising a pairwise similarity between a pair of data points within a plurality of data points in the dataset; defining a fully-connected graph comprising a plurality of nodes and a plurality of edges connecting at least a portion of the plurality of nodes with other nodes of the plurality, each edge of the plurality of edges having a weight equal to the kernel between a corresponding pair of data points, wherein the graph has an adjacency matrix that is equivalent to the kernel matrix; computing a plurality of eigenvalues for the kernel matrix; selecting a first eigenvector corresponding to the smallest non-zero eigenvalue of the plurality of eigenvalues; bisecting the dataset into two classes using the first eigenvector; aligning the kernels using a second eigenvector so that the two classes have equal probability; and selecting an optimized kernel for use in the learning machine, wherein the optimized kernel produces maximal kernel alignment.