Large scale spectral clustering using resistance distance and spielman-teng solvers

Nguyen Lu Dang Khoa, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

The promise of spectral clustering is that it can help detect complex shapes and intrinsic manifold structure in large and high dimensional spaces. The price for this promise is the computational cost O(n 3) for computing the eigen-decomposition of the graph Laplacian matrix-so far a necessary subroutine for spectral clustering. In this paper we bypass the eigen-decomposition of the original Laplacian matrix by leveraging the recently introduced Spielman and Teng near-linear time solver for systems of linear equations and random projection. Experiments on several synthetic and real datasets show that the proposed approach has better clustering quality and is faster than the state-of-the-art approximate spectral clustering methods.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages7-21
Number of pages15
Volume7569 LNAI
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event15th International Conference on Discovery Science, DS 2012 - Lyon
Duration: 29 Oct 201231 Oct 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7569 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other15th International Conference on Discovery Science, DS 2012
CityLyon
Period29/10/1231/10/12

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Keywords

  • random projection
  • resistance distance
  • spectral clustering
  • Spielman-Teng Solver

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Khoa, N. L. D., & Chawla, S. (2012). Large scale spectral clustering using resistance distance and spielman-teng solvers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7569 LNAI, pp. 7-21). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7569 LNAI). https://doi.org/10.1007/978-3-642-33492-4_4