Prof. Kai Ming Ting
School of Science, Engineering and Information Technology, Federation University (Australia)
Title
Isolation Kernel and Its Impacts on SVM and Density-based Clustering
Abstract
This talk reports two recent works on data dependent kernels that are derived directly from data.
Data dependent kernels have been an outstanding issue for about two decades which hampered the development of kernel-based methods.
Isolation Kernel is a new data-dependent kernel which is solely dependent on data distribution, requiring neither class information nor explicit learning.
In contrast, existing data dependent kernels rely heavily on class information and explicit learning.
In one implementation, Isolation Kernel approximates well to a data independent kernel function called Laplacian kernel under uniform density distribution.
With this revelation, Isolation Kernel can be viewed as a data dependent kernel that adapts a data independent kernel to the structure of a dataset.
Isolation Kernel has been shown to enable SVM and DBSCAN to improve their task-specific performances by simply replacing the commonly used kernel/distance with Isolation Kernel, leaving the rest of the procedures unchanged.
We show for the first time that the clustering performance of the classic density-based clustering algorithm DBSCAN (published in KDD 1996) can be significantly uplifted to surpass that of the recent density-peak clustering algorithm (published in Science 2014).
Brief Bio
After receiving his PhD from the University of Sydney, Kai Ming Ting had worked at the University of Waikato, Deakin University and Monash University.
He joins Federation University Australia since 2014. He had previously held visiting positions at Osaka University, Nanjing University, and Chinese University of Hong Kong.
His current research interests are in the areas of mass estimation, mass-based or data dependent similarity, anomaly detection, ensemble approaches, data streams,
data mining and machine learning in general. He has served as a program committee co-chair for the Twelfth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2008).
He was a member of the program committee for a number of international conferences including ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,
and International Conference on Machine Learning. He has received research funding from Australian Research Council, US Air Force of Scientific Research (AFOSR/AOARD),
Toyota InfoTechnology Center, and Australian Institute of Sports.
Awards received include the Runner-up Best Paper Award in 2008 IEEE ICDM (for Isolation Forest), and the Best Paper Award in 2006 PAKDD.
He is the one of the creators of isolation techniques, mass-based similarity and isolation kernel.
