Title: Similarity Search Over Graphs Using Localized Spectral Analysis,
Abstract: This paper provides a new similarity detection algorithm. Given an input set
of multi-dimensional data points, where each data point is assumed to be
multi-dimensional, and an additional reference data point for similarity
finding, the algorithm uses kernel method that embeds the data points into a
low dimensional manifold. Unlike other kernel methods, which consider the
entire data for the embedding, our method selects a specific set of kernel
eigenvectors. The eigenvectors are chosen to separate between the data points
and the reference data point so that similar data points can be easily
identified as being distinct from most of the members in the dataset.