Similarity search over graphs using localized spectral analysis

@article{Aizenbud2017SimilaritySO,
  title={Similarity search over graphs using localized spectral analysis},
  author={Yariv Aizenbud and Amir Averbuch and Gil Shabat and Guy Ziv},
  journal={2017 International Conference on Sampling Theory and Applications (SampTA)},
  year={2017},
  pages={635-638}
}
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… Expand

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