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.
@article{arxiv.1707.03311,
title = {Similarity Search Over Graphs Using Localized Spectral Analysis},
author = {Yariv Aizenbud and Amir Averbuch and Gil Shabat and Guy Ziv},
journal= {arXiv preprint arXiv:1707.03311},
year = {2017}
}