Incremental spectral clustering by efficiently updating the eigensystem


07-Nov-2017 05:10

The proposed technique relies on a kernel-based formulation of the spectral clustering problem, also known as kernel spectral clustering.

In this framework, the Nyström approximation of the feature map of size Rocco Langone was born in Potenza, Italy, in 1983.

He has served as a Director and Organizer of the NATO Advanced Study Institute on Learning Theory and Practice (Leuven 2002), as a program co-chair for the International Joint Conference on Neural Networks 2004 and the International Symposium on Nonlinear Theory and its Applications 2005, as an organizer of the International Symposium on Synchronization in Complex Networks 2007, a co-organizer of the NIPS 2010 workshop on Tensors, Kernels and Machine Learning, and chair of ROKS 2013.

He has been awarded an ERC Advanced Grant 2011 and has been elevated IEEE Fellow 2015 for developing least squares support vector machines.

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The proposed approach shows its high accuracy and efficiency in many synthetic and real datasets and takes only 8 milliseconds on average to detect anomalies online on the DBLP graph which has more than 600,000 nodes and 2 millions edges.

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He was a Ph D fellow in machine learning from 2010 to 2014 and after, for two years, a postdoctoral researcher in machine learning with the STADIUS Research Division, Department of Electrical Engineering, KU Leuven.

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In this period his research focused on kernel methods, optimization, unsupervised learning (clustering and community detection), big data, fault detection. Suykens was born in Willebroek Belgium, May 18 1966.

He is currently a data scientist at Deloitte Belgium where he builds machine learning models for several business applications. He received the master degree in Electro-Mechanical Engineering and the Ph D degree in Applied Sciences from the Katholieke Universiteit Leuven, in 19, respectively.