TY - GEN
T1 - Enhanced distance subset approximation using class-specific subspace kernel representation for kernel approximation
AU - Yu, Yinan
AU - Diamantaras, Konstantinos I.
AU - McKelvey, Tomas
AU - Kung, S. Y.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/8
Y1 - 2016/11/8
N2 - 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.
AB - 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.
KW - Kernel approximation
KW - class-specific subspace model
KW - classification
KW - discriminative representation
UR - http://www.scopus.com/inward/record.url?scp=85002002557&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85002002557&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2016.7738811
DO - 10.1109/MLSP.2016.7738811
M3 - Conference contribution
AN - SCOPUS:85002002557
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
BT - 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
A2 - Diamantaras, Kostas
A2 - Uncini, Aurelio
A2 - Palmieri, Francesco A. N.
A2 - Larsen, Jan
PB - IEEE Computer Society
T2 - 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
Y2 - 13 September 2016 through 16 September 2016
ER -