Prof. Wensheng Zhang, University of the Chinese Academy of Sciences, China
张文生, 中国科学院大学, 中国
Prof. Wensheng Zhang received the PhD degree in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences (CAS), in 2000. He joined the Institute of Software, CAS, in 2001. He is a professor of machine learning, data mining and the director of Research and Development Department, Institute of Automation, CAS. His research interests include computer vision, pattern recognition and artificial intelligence.
Speech Title: Tensor Multi-Elastic Kernel Self-Paced Learning for Time Series Clustering
Abstract: The unique characteristics of time series, including high-dimension, warping and the integration of multiple elastic measures, pose challenges for the present clustering algorithms, most of which take into account only part of these difficulties. We make an effort to simultaneously address all aforementioned issues in time series clustering under a unified multiple kernels clustering (MKC) framework. Specifically, we first implicitly map the raw time series space into multiple kernel spaces via elastic distance measure functions. In such high-dimensional spaces, we resort to the tensor constraint based self-representation subspace clustering approach, involving in the self-paced learning paradigm, to explore the essential low-dimensional structure of the data, as well as the high-order complementary information from different elastic kernels. Extensive experiments on 85 univariate and 10 multivariate time series datasets demonstrate the significant superiority of the proposed approach beyond the baseline and several state-of-the-art MKC methods.