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学术报告

【数学学院学术报告】Atomic Representation-based Classification: Theory, Algorithm and Applications
发布日期:2018-12-26  浏览量:

题目:Atomic Representation-based Classification: Theory, Algorithm and Applications

报告人:李落清(湖北大学)

报告时间:2018年12月28日下午2:30-3:30

报告地点:主楼321

摘要:Representation-based classification (RC) methods such as sparse RC (SRC) have attracted great interest in pattern recognition recently. In this talk, we introduce a new condition called atomic classification condition (ACC), which reveals important geometric insights for the theory of ARC. We establish the theoretical guarantees for a general unified framework termed as atomic representation-based classification (ARC), which includes most RC methods as special cases. We show that under such condition ARC is provably effective in correctly recognizing

any new test sample, even corrupted with noise. Numerical results are provided to validate and complement our theoretical analysis of ARC and its important special cases for both noiseless and noisy test data.

报告人简介:李落清,男,理学博士。湖北大学数学与统计学学院教授,博士生导师。从事逼近论及其应用的教学和研究工作。主要研究兴趣:函数逼近与小波分析、时频分析与信号处理、学习理论与模式识别。担任小波分析及其应用国际学术会议程序委员会主席和小波分析与模式识别国际学术会议程序委员会主席。现任国际学术刊物《International Journal of Wavelets, Multiresolution and Information Processing》执行主编 (Managing Editor)。

邀请人:陈迪荣

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