TY - GEN
T1 - CoRE kernels
AU - Li, Ping
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84923294292&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84923294292&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84923294292
T3 - Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014
SP - 496
EP - 504
BT - Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014
A2 - Zhang, Nevin L.
A2 - Tian, Jin
PB - AUAI Press
T2 - 30th Conference on Uncertainty in Artificial Intelligence, UAI 2014
Y2 - 23 July 2014 through 27 July 2014
ER -