Similarity search over graphs using localized spectral analysis

  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)},
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


Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
This work proposes a geometrically motivated algorithm for representing the high-dimensional data that provides a computationally efficient approach to nonlinear dimensionality reduction that has locality-preserving properties and a natural connection to clustering. Expand
Diffusion maps
In this paper, we provide a framework based upon diffusion processes for finding meaningful geometric descriptions of data sets. We show that eigenfunctions of Markov matrices can be used toExpand
Similarity Search in High Dimensions via Hashing
Experimental results indicate that the novel scheme for approximate similarity search based on hashing scales well even for a relatively large number of dimensions, and provides experimental evidence that the method gives improvement in running time over other methods for searching in highdimensional spaces based on hierarchical tree decomposition. Expand
Robust Hashing With Local Models for Approximate Similarity Search
This paper proposes a novel hashing method, namely, robust hashing with local models (RHLM), which learns a set of robust hash functions to map the high-dimensional data points into binary hash codes by effectively utilizing local structural information. Expand
Multiscale Anomaly Detection Using Diffusion Maps
  • Gal Mishne, I. Cohen
  • Mathematics, Computer Science
  • IEEE Journal of Selected Topics in Signal Processing
  • 2013
A multiscale approach to anomaly detection in images, combining spectral dimensionality reduction and a nearest-neighbor-based anomaly score is proposed, based on Gaussian pyramid representation, which drives the sampling process to ensure separability of the anomaly from the background clutter. Expand
Hashing for Similarity Search: A Survey
This paper presents a survey on one of the main solutions to approximate search, hashing, which has been widely studied since the pioneering work locality sensitive hashing, and divides the hashing algorithms into two main categories. Expand
Kernel Nearest-Neighbor Algorithm
Experiments show that kernel nearest-neighbor algorithm is more powerful than conventional nearest-northern neighbour algorithm, and it can compete with SVM. Expand
Image registration by local histogram matching
  • D. Shen
  • Computer Science
  • Pattern Recognit.
  • 2007
By hierarchically matching new attribute vectors, the proposed method can perform as successfully as the previous HAMMER algorithm did in registering MR brain images, while providing more generalized applications in registering images of various organs. Expand
Matrix Decompositions using sub-Gaussian Random Matrices
In recent years, several algorithms, which approximate matrix decomposition, have been developed. These algorithms are based on metric conservation features for linear spaces of random projectionExpand
An Eigenvector-Based Corresponding Points Auto-Detection Algorithm for Non-Rigid Registration of CT Brain Images
CT brain images have advantage on the detection of brain tumor and cerebral hemorrhage. To provide integrated scheme of computer-aided diagnosis of these diseases with CT images, the non-rigidExpand