CoRE kernels

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

Abstract

The term "CoRE kernel" stands for correlation resemblance kernel. In many real-world applications (e.g., computer vision), the data are often high-dimensional, sparse, and non-binary. We propose two types of (nonlinear) CoRE kernels for non-binary sparse data and demonstrate the effectiveness of the new kernels through a classification experiment. CoRE kernels are simple with no tuning parameters. However, training nonlinear kernel SVM can be costly in time and memory and may not be always suitable for truly large-scale industrial applications (e.g., search). In order to make the proposed CoRE kernels more practical, we develop basic probabilistic hashing (approximate) algorithms which transform nonlinear kernels into linear kernels.

Original languageEnglish (US)
Title of host publicationUncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014
EditorsNevin L. Zhang, Jin Tian
PublisherAUAI Press
Pages496-504
Number of pages9
ISBN (Electronic)9780974903910
Publication statusPublished - Jan 1 2014
Event30th Conference on Uncertainty in Artificial Intelligence, UAI 2014 - Quebec City, Canada
Duration: Jul 23 2014Jul 27 2014

Publication series

NameUncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014

Other

Other30th Conference on Uncertainty in Artificial Intelligence, UAI 2014
CountryCanada
CityQuebec City
Period7/23/147/27/14

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Li, P. (2014). CoRE kernels. In N. L. Zhang, & J. Tian (Eds.), Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014 (pp. 496-504). (Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014). AUAI Press.