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题 目： Mining Sparse Learning for Big Data.
报告人：Professor Ivor W Tsang
Ivor W Tsang is a Future Fellow and Associate Professor with the Centre for Quantum Computation & Intelligent Systems (QCIS), at the University of Technology, Sydney. His research focuses on kernel methods, transfer learning, feature selection, big data analytics for data with trillions of dimensions, and their applications to computer vision and pattern recognition. He has more than 100 research papers published in top-tier journals and conference proceedings.
The world continues to generate quintillion bytes of data daily, leading to pressing needs for new endeavours to deal with the grad challenges brought about by Big Data. The massive sample size and ultrahigh dimensionality of big data not only incur the storage disaster, but also bring the scalability issues. In this talk, I will first present a novel sparse learning framework to address the issues brought by Big Data and ultrahigh dimensional problems. The proposed learning framework would handle tens of million data easily and is significantly faster than state-of-the-art L1-regularzied methods by several orders of magnitudes. Finally, several applications of the proposed framework, such as face recognition, video analysis and transfer learning will be presented