@inproceedings{ff3476f001154e07af8c346e0c696d22,
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 points1 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 considers 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.",
author = "Yariv Aizenbud and Amir Averbuch and Gil Shabat and Guy Ziv",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 12th International Conference on Sampling Theory and Applications, SampTA 2017 ; Conference date: 03-07-2017 Through 07-07-2017",
year = "2017",
month = sep,
day = "1",
doi = "https://doi.org/10.1109/SAMPTA.2017.8024435",
language = "الإنجليزيّة",
series = "2017 12th International Conference on Sampling Theory and Applications, SampTA 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "635--638",
editor = "Gholamreza Anbarjafari and Andi Kivinukk and Gert Tamberg",
booktitle = "2017 12th International Conference on Sampling Theory and Applications, SampTA 2017",
address = "الولايات المتّحدة",
}