Embedding heterogeneous data by preserving multiple kernels

Mehmet Gönen

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

1 Scopus citations


Heterogeneous data may arise in many real-life applications under different scenarios. In this paper, we formulate a general framework to address the problem of modeling heterogeneous data. Our main contribution is a novel embedding method, called multiple kernel preserving embedding (MKPE), which projects heterogeneous data into a unified embedding space by preserving crossdomain interactions and within-domain similarities simultaneously. These interactions and similarities between data points are approximated with Gaussian kernels to transfer local neighborhood information to the projected subspace. We also extend our method for out-of-sample embedding using a parametric formulation in the projection step. The performance of MKPE is illustrated on two tasks: (i) modeling biological interaction networks and (ii) cross-domain information retrieval. Empirical results of these two tasks validate the predictive performance of our algorithm.

Original languageEnglish (US)
Title of host publicationECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings
EditorsTorsten Schaub, Gerhard Friedrich, Barry O'Sullivan
PublisherIOS Press
Number of pages6
ISBN (Electronic)9781614994183
StatePublished - Jan 1 2014
Event21st European Conference on Artificial Intelligence, ECAI 2014 - Prague, Czech Republic
Duration: Aug 18 2014Aug 22 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389


Other21st European Conference on Artificial Intelligence, ECAI 2014
Country/TerritoryCzech Republic

ASJC Scopus subject areas

  • Artificial Intelligence


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