Index-driven similarity search in metric spaces (Survey Article)

By Gísli R. Hjaltason, Hanan Samet. TODS, 2003

489 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/745f8e36dc31281d3f2b94ad4dbbe1d940e0d4e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Signal Processing

Citations in the corpus

A Complexity-Invariant Distance Measure for Time Series (2011, background): “However, many other indexing schemes are strongly related [8].”
Nearest-Neighbor Searching and Metric Space Dimensions (2005, background): “In the database or information-retrieval literature, it might be called the problem of building an index for similarity search [ HS03 ].”
Distance based similarity models for content based multimedia retrieval (2013, background): “By relating the distances of three objects with each other, the triangle inequality allows to derive a powerful lower bound for metric indexing approaches [Zezula et al., 2006, Samet, 2006, Hjaltason and Samet, 2003, Chávez et al., 2001].”
Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search (2007, unknown): “Please see [7, 15, 12] for some survey.”
Succinct nearest neighbor search (2011, methodology): “other topics for exact proximity searching [7, 21, 12, 4].”
Effective Proximity Retrieval by Ordering Permutations (2008, background): “In [16], [ 27 ], [38], [44], most of those approaches are surveyed and explained in detail.”
Efficient Processing of k Nearest Neighbor Joins using MapReduce (2012, background): “[8] Given two pivots p i , p j , let HP ( p i , p j ) be the generalized hyperplane, where any object o lying on HP ( p i , p j ) has the equal distance to p i and p j .”
Navigating K-Nearest Neighbor Graphs to Solve Nearest Neighbor Searches (2010, background): “The NNS problem, the curse of dimensionality, a large number of indexes, and the problems found for exact nearest neighbor searching are reported in several places and surveyed in [3,4,5].”
Dynamic Time Warping Algorithm Review (2008, methodology): “The SVD decomposition, the Fourier transform (and the similar Discrete Cosine transform), the Wavelet decomposition, Multidimensional Scaling, random projection techniques, FastMap (and variants) are the methods usually used, [27].”