A Monte Carlo algorithm for fast projective clustering

Published

Journal Article

We propose a mathematical formulation for the notion of optimal projective cluster, starting from natural requirements on the density of points in subspaces. This allows us to develop a Monte Carlo algorithm for iteratively computing projective clusters. We prove that the computed clusters are good with high probability. We implemented a modified version of the algorithm, using heuristics to speed up computation. Our extensive experiments show that our method is significantly more accurate than previous approaches. In particular, we use our techniques to build a classifier for detecting rotated human faces in cluttered images.

Duke Authors

Cited Authors

  • Procopiuc, CM; Jones, M; Agarwal, PK; Murali, TM

Published Date

  • September 17, 2002

Published In

Start / End Page

  • 418 - 427

International Standard Serial Number (ISSN)

  • 0730-8078

Citation Source

  • Scopus