Keynote Speakers

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.

张文生
,中国科学院自动化研究所研究员、博士生导师、副总工程师,中国科学院大学人工智能首席教授,中科麦迪健康医疗人工智能研究院院长,科技创新2030—“新一代人工智能”重大项目首席科学家。历任科技处长、重点项目处长、所务委员、副总工程师。主要研究:人工智能、机器学习、大数据模式挖掘、跨模态数据标注、医疗数据分析推理。国家“云计算和大数据”、“物联网与智慧城市”重点研发专项总体组专家,中国仪器仪表学会物联网工作委员会副理事长、中国人工智能学会智能服务专委会副主任。发表论文200余篇,国内外发明专利50余项,国家二等奖1项,省部级二等奖5项。

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.