Approximate furthest neighbor with application to annulus query
TLDR
In this article, a data structure for answering approximate nearest neighbor queries in high-dimensional Euclidean space has been proposed based on the technique of Indyk (SODA 2003), storing random projections to provide sublinear query time.About:
This article is published in Information Systems.The article was published on 2017-03-01 and is currently open access. It has received 12 citations till now. The article focuses on the topics: Locality-sensitive hashing & k-nearest neighbors algorithm.read more
Citations
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Journal ArticleDOI
Two Efficient Hashing Schemes for High-Dimensional Furthest Neighbor Search
TL;DR: A novel concept of the Reverse Locality-Sensitive Hashing (RLSH) family which is directly designed for inline-formula-based hashing schemes and applies a data-dependent objects selection to largely reduce the number of data objects.
Book ChapterDOI
Interactive Learning for Multimedia at Large
Omar Shahbaz Khan,Björn Þór Jónsson,Björn Þór Jónsson,Stevan Rudinac,Jan Zahálka,Hanna Ragnarsdóttir,Þórhildur Þorleiksdóttir,Gylfi Þór Guðmundsson,Laurent Amsaleg,Marcel Worring +9 more
TL;DR: This work proposes an interactive learning approach that builds on and extends the state of the art in user relevance feedback systems and high-dimensional indexing for multimedia, and outperforms the relevant state-of-the-art approaches in terms of interactive performance.
Proceedings ArticleDOI
Distance-Sensitive Hashing
TL;DR: This paper begins the study of distance-sensitive hashing (DSH), a generalization of LSH that seeks a family of hash functions such that the probability of two points having the same hash value is a given function of the distance between them, and extends existing LSH lower bounds, showing that they also hold in the asymmetric setting.
Posted Content
Distance-Sensitive hashing
TL;DR: Distance-sensitive hashing (DSH) as discussed by the authors is a generalization of LSH that seeks a family of hash functions such that the probability of two points having the same hash value is a given function of the distance between them.
Proceedings ArticleDOI
Point-to-Hyperplane Nearest Neighbor Search Beyond the Unit Hypersphere
TL;DR: This paper introduces a new asymmetric transformation and develops the first two provable hyperplane hashing schemes, Nearest Hyperplane hashing (NH) and Furthest Hyperplanes hashing (FH), for high-dimensional P2HNNS beyond the unit hypersphere.
References
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Book
Computational Geometry: Algorithms and Applications
TL;DR: In this article, an introduction to computational geometry focusing on algorithms is presented, which is related to particular applications in robotics, graphics, CAD/CAM, and geographic information systems.
Proceedings ArticleDOI
Approximate nearest neighbors: towards removing the curse of dimensionality
Piotr Indyk,Rajeev Motwani +1 more
TL;DR: In this paper, the authors present two algorithms for the approximate nearest neighbor problem in high-dimensional spaces, for data sets of size n living in R d, which require space that is only polynomial in n and d.
Journal ArticleDOI
The MovieLens Datasets: History and Context
TL;DR: The history of MovieLens and the MovieLens datasets is documents, including a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization, and best practices and limitations of using the Movie Lens datasets in new research are documented.
Proceedings ArticleDOI
Locality-sensitive hashing scheme based on p-stable distributions
TL;DR: A novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under lp norm, based on p-stable distributions that improves the running time of the earlier algorithm and yields the first known provably efficient approximate NN algorithm for the case p<1.
Journal ArticleDOI
Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality
TL;DR: Two algorithms for the approximate nearest neighbor problem in high dimensional spaces for data sets of size n living in IR are presented, achieving query times that are sub-linear in n and polynomial in d.