Enhanced distance subset approximation using class-specific subspace kernel representation for kernel approximation

Yinan Yu, Konstantinos I. Diamantaras, Tomas McKelvey, S. Y. Kung

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

Abstract

The computational complexity of kernel methods grows at least quadratically with respect to the training size and hence low rank kernel approximation techniques are commonly used. One of the most popular approximations is constructed by sub-sampling the training data. In this paper, we present a sampling algorithm called Enhanced Distance Subset Approximation (EDSA) based on a novel kernel function called CLAss-Specific Kernel (CLASK), which applies the idea of subspace clustering to low rank kernel approximation. By representing the kernel matrix based on a class-specific subspace model, it is allowed to use distinct kernel functions for different classes, which provides a better flexibility compared to classical kernel approximation techniques. Experimental results conducted on various UCI datasets are provided in order to verify the proposed techniques.

Original languageEnglish (US)
Title of host publication2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
EditorsKostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
PublisherIEEE Computer Society
ISBN (Electronic)9781509007462
DOIs
StatePublished - Nov 8 2016
Externally publishedYes
Event26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
Duration: Sep 13 2016Sep 16 2016

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2016-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
CountryItaly
CityVietri sul Mare, Salerno
Period9/13/169/16/16

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing

Keywords

  • Kernel approximation
  • class-specific subspace model
  • classification
  • discriminative representation

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  • Cite this

    Yu, Y., Diamantaras, K. I., McKelvey, T., & Kung, S. Y. (2016). Enhanced distance subset approximation using class-specific subspace kernel representation for kernel approximation. In K. Diamantaras, A. Uncini, F. A. N. Palmieri, & J. Larsen (Eds.), 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings [7738811] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2016-November). IEEE Computer Society. https://doi.org/10.1109/MLSP.2016.7738811