Anti-sparse coding for approximate nearest neighbor search

10/17/2011
by   Hervé Jégou, et al.
0

This paper proposes a binarization scheme for vectors of high dimension based on the recent concept of anti-sparse coding, and shows its excellent performance for approximate nearest neighbor search. Unlike other binarization schemes, this framework allows, up to a scaling factor, the explicit reconstruction from the binary representation of the original vector. The paper also shows that random projections which are used in Locality Sensitive Hashing algorithms, are significantly outperformed by regular frames for both synthetic and real data if the number of bits exceeds the vector dimensionality, i.e., when high precision is required.

READ FULL TEXT
research
11/24/2020

Efficient Approximate Nearest Neighbor Search for Multiple Weighted l_p≤2 Distance Functions

Nearest neighbor search is fundamental to a wide range of applications. ...
research
10/07/2022

Toward an Over-parameterized Direct-Fit Model of Visual Perception

In this paper, we revisit the problem of computational modeling of simpl...
research
02/21/2016

2-Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search

The method of random projections has become a standard tool for machine ...
research
01/26/2017

Sparse Ternary Codes for similarity search have higher coding gain than dense binary codes

This paper addresses the problem of Approximate Nearest Neighbor (ANN) s...
research
02/08/2022

Orthogonal Matrices for MBAT Vector Symbolic Architectures, and a "Soft" VSA Representation for JSON

Vector Symbolic Architectures (VSAs) give a way to represent a complex o...
research
08/09/2017

Random Binary Trees for Approximate Nearest Neighbour Search in Binary Space

Approximate nearest neighbour (ANN) search is one of the most important ...
research
04/18/2019

Global Hashing System for Fast Image Search

Hashing methods have been widely investigated for fast approximate neare...

Please sign up or login with your details

Forgot password? Click here to reset