Localized data fusion for kernel k-means clustering with application to cancer biology

Research output: Contribution to journalConference article

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Abstract

In many modern applications from, for example, bioinformatics and computer vision, samples have multiple feature representations coming from different data sources. Multiview learning algorithms try to exploit all these available information to obtain a better learner in such scenarios. In this paper, we propose a novel multiple kernel learning algorithm that extends kernel k-means clustering to the multiview setting, which combines kernels calculated on the views in a localized way to better capture sample-specific characteristics of the data. We demonstrate the better performance of our localized data fusion approach on a human colon and rectal cancer data set by clustering patients. Our method finds more relevant prognostic patient groups than global data fusion methods when we evaluate the results with respect to three commonly used clinical biomarkers.

Original languageEnglish (US)
Pages (from-to)1305-1313
Number of pages9
JournalAdvances in Neural Information Processing Systems
Volume2
Issue numberJanuary
StatePublished - Jan 1 2014
Event28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada
Duration: Dec 8 2014Dec 13 2014

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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