Improving k-NN graph accuracy using local intrinsic dimensionality

Document Type

Conference Proceeding

Publication Date

1-1-2017

Abstract

The k-nearest neighbor (k-NN) graph is an important data structure for many data mining and machine learning applications. The accuracy of k-NN graphs depends on the object feature vectors, which are usually represented in high-dimensional spaces. Selecting the most important features is essential for providing compact object representations and for improving the graph accuracy. Having a compact feature vector can reduce the storage space and the computational complexity of search and learning tasks. In this paper, we propose NNWID-Descent, a similarity graph construction method that utilizes the NNF-Descent framework while integrating a new feature selection criterion, Support-Weighted Intrinsic Dimensionality, that estimates the contribution of each feature to the overall intrinsic dimensionality. Through extensive experiments on various datasets, we show that NNWID-Descent allows a significant amount of local feature vector sparsification while still preserving a reasonable level of graph accuracy.

Identifier

85031303591 (Scopus)

ISBN

[9783319684734]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/978-3-319-68474-1_8

e-ISSN

16113349

ISSN

03029743

First Page

110

Last Page

124

Volume

10609 LNCS

Grant

25240036

Fund Ref

Japan Society for the Promotion of Science

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