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学术交流报告

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2015-05-11 10:10  mg游戏平台官网: 次

人:悉尼科技大学Ivor W Tsang博士

报告时间:2015515日下午4:00

报告地点:224报告厅

报告题目:Large Margin Metric Learning for Multi-label Prediction

报告人概况:

Ivor WTsang 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. In 2009, Dr Tsang was conferred the 2008 Natural Science Award (Class II) by Ministry of Education, China, which recognized his contributions to kernel methods. In 2013, Dr Tsang received his prestigious Australian Research Council Future Fellowship for his research regarding Machine Learning on Big Data. In addition, he had received the prestigious IEEE Transactions on Neural Networks Outstanding 2004 Paper Award in 2007, the 2014 IEEE Transactions on Multimedia Prize Paper Award, and a number of best paper awards and honors from reputable international conferences, including the Best Student Paper Award at CVPR 2010, and the Best Paper Award at ICTAI 2011. He was also awarded the 微软 Fellowship 2005. Dr Tsang is currently serving as an Area Chair at NIPS 2015, and Senior Program Committee at IJCAI 2011-2015.

报告摘要:

Canonical correlation analysis (CCA) and maximum margin output coding (MMOC) methods have shown promising results for multi-label prediction, where each instance is associated with multiple labels. However, these methods require an expensive decoding procedure to recover the multiple labels of each testing instance. The testing complexity becomes unbearable when there are many labels. To avoid decoding completely, we present a novel large margin metric learning paradigm for multi-label prediction. Particularly, the proposed method learns a distance metric to discover label dependency such that instances with very different multiple labels will be moved far away. To handle many labels, we further present an accelerated proximal gradient procedure to speed up the learning process. Comprehensive experiments demonstrate that our proposed method is significantly faster than CCA and MMOC in terms of both training and testing complexities. Moreover, our method achieves superior prediction performance compared with state-of-the-art methods.

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