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Seminar abstract

Towards optimal multi-instance dictionary for multivariate performance measures

Kai Ming Ting
Federation University Australia

Abstract: Conventional wisdom in machine learning says that all algorithms are expected to follow the trajectory of a learning curve which is often colloquially referred to as ‘more data the better’. We call this ‘the gravity of learning curve’, and it is assumed that no learning algorithms are ‘gravity-defiant’. Contrary to the conventional wisdom, this talk provides the theoretical analysis and the empirical evidence that nearest neighbour anomaly detectors are gravity-defiant algorithms. In the age of big data, the revelation and the knowledge about the gravity-defiant behaviour discovered have two impacts. First, the capacity provided by big data infrastructures would be overkill because the gravity-defiant algorithms that produce good performing models using small datasets can be executed comfortably in existing computing infrastructures. Second, it opens a whole new direction of research into different types of gravity-defiant algorithms which can achieve high performance with small sample size.

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, 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 creator of isolation techniques, mass estimation and mass-based dissimilarity.
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