Outlier-aware robust clustering

Pedro A. Forero, Vassilis Kekatos, Georgios B. Giannakis

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

4 Scopus citations

Abstract

Clustering is a basic task in a variety of machine learning applications. Partitioning a set of input vectors into compact, well-separated subsets can be severely affected by the presence of model-incompatible inputs called outliers. The present paper develops robust clustering algorithms for jointly partitioning the data and identifying the outliers. The novel approach relies on translating scarcity of outliers to sparsity in a judiciously defined domain, to robustify three widely used clustering schemes: hard K-means, fuzzy K-means, and probabilistic clustering. Cluster centers and assignments are iteratively updated in closed form. The developed outlier-aware algorithms are guaranteed to converge, while their computational complexity is of the same order as their outlier-agnostic counterparts. Preliminary simulations validate the analytical claims.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages2244-2247
Number of pages4
DOIs
StatePublished - Aug 18 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period5/22/115/27/11

Keywords

  • K-means
  • block coordinate descent
  • convex relaxation
  • expectation maximization
  • robust clustering

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