Enhanced Distance Subset Approximation using Class-Specific Subspace Kernel Representation for Kernel Approximation
Paper in proceedings, 2016

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.

classification

discriminative representation

Kernel approximation

class-specific subspace model

Author

Yinan Yu

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Konstantinos I. Diamantaras

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Tomas McKelvey

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

S.Y. Kung

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Proceeding of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016), Vietri sul Mare, Italy

2161-0371 (eISSN)

Vol. 2016-November

Areas of Advance

Information and Communication Technology

Subject Categories

Information Science

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/MLSP.2016.7738811

ISBN

978-1-5090-0746-2

More information

Latest update

7/23/2018