Exact and/or Fast Nearest Neighbors

10/06/2019
by   Matthew Francis-Landau, et al.
0

Prior methods for retrieval of nearest neighbors in high dimensions are fast and approximate–providing probabilistic guarantees of returning the correct answer–or slow and exact performing an exhaustive search. We present Certified Cosine, a novel approach to nearest-neighbors which takes advantage of structure present in the cosine similarity distance metric to offer certificates. When a certificate is constructed, it guarantees that the nearest neighbor set is correct, possibly avoiding an exhaustive search. Certified Cosine's certificates work with high dimensional data and outperform previous exact nearest neighbor methods on these datasets.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/05/2020

Fast top-K Cosine Similarity Search through XOR-Friendly Binary Quantization on GPUs

We explore the use of GPU for accelerating large scale nearest neighbor ...
research
02/22/2021

Manifold learning with approximate nearest neighbors

Manifold learning algorithms are valuable tools for the analysis of high...
research
05/05/2014

Comparing apples to apples in the evaluation of binary coding methods

We discuss methodological issues related to the evaluation of unsupervis...
research
05/04/2017

Fast k-means based on KNN Graph

In the era of big data, k-means clustering has been widely adopted as a ...
research
11/07/2021

Parallel Nearest Neighbors in Low Dimensions with Batch Updates

We present a set of parallel algorithms for computing exact k-nearest ne...
research
12/22/2017

A Model of Optimal Network Structure for Decentralized Nearest Neighbor Search

One of the approaches for the nearest neighbor search problem is to buil...
research
12/05/2021

Learning Query Expansion over the Nearest Neighbor Graph

Query Expansion (QE) is a well established method for improving retrieva...

Please sign up or login with your details

Forgot password? Click here to reset