Multiple kernel learning clustering with an application to malware

Blake Anderson, Curtis Storlie, Terran Lane

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

7 Scopus citations

Abstract

With the increasing prevalence of richer, more complex data sources, learning with multiple views is becoming more widespread. Multiple kernel learning (MKL) has been developed to address this problem, but in general, the solutions provided by traditional MKL are restricted to a classification objective function. In this work, we develop a novel multiple kernel learning algorithm that is based on a spectral clustering objective function which is able to find an optimal kernel weight vector for the clustering problem. We go on to show how this optimization problem can be cast as a semidefinite program and efficiently solved using off-the-shelf interior point methods.

Original languageEnglish (US)
Title of host publicationProceedings - 12th IEEE International Conference on Data Mining, ICDM 2012
Pages804-809
Number of pages6
DOIs
StatePublished - Dec 1 2012
Event12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgium
Duration: Dec 10 2012Dec 13 2012

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other12th IEEE International Conference on Data Mining, ICDM 2012
CountryBelgium
CityBrussels
Period12/10/1212/13/12

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Keywords

  • Malware
  • Multiple kernel learning
  • Semidefinite programming
  • Spectral clustering

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Anderson, B., Storlie, C., & Lane, T. (2012). Multiple kernel learning clustering with an application to malware. In Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012 (pp. 804-809). [6413849] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2012.75