A graph-based N-body approximation with application to stochastic neighbor embedding

Eli Parviainen*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)

Abstract

We propose a novel approximation technique, bubble approximation (BA), for repulsion forces in an N-body problem, where attraction has a limited range and repulsion acts between all points. These kinds of systems occur frequently in dimension reduction and graph drawing. Like tree codes, the established N-body approximation method, BA replaces several point-to-point computations by one area-to-point computation. Novelty of BA is to consider not only the magnitudes but also the directions of forces from the area. Therefore, its area-to-point approximations are applicable anywhere in the space. The joint effect of forces from inside the area is calculated analytically, assuming a homogeneous mass of points inside the area. These two features free BA from hierarchical data structures and complicated bookkeeping of interactions, which plague tree codes. Instead, BA uses a simple graph to control the computations. The graph provides a sparse matrix, which, suitably weighted, replaces the full matrix of pairwise comparisons in the N-body problem. As a concrete example, we implement a sparse-matrix version of stochastic neighbor embedding (a dimension reduction method), and demonstrate its good performance by comparisons to full-matrix method, and to three different approximate versions of the same method.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalNeural Networks
Volume75
DOIs
Publication statusPublished - 1 Mar 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • Approximation method
  • Dimension reduction
  • Machine learning
  • N-body problem
  • Stochastic neighbor embedding

Fingerprint Dive into the research topics of 'A graph-based N-body approximation with application to stochastic neighbor embedding'. Together they form a unique fingerprint.

Cite this