报告人：李落清教授（湖北大学数学与统计学院）

报告时间：2024年4月17日（周三）下午16:30-17:30

报告地点：数学与信息学院西楼数学系605

报告摘要：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。主要研究兴趣：时频分析与信号处理、学习理论与模式识别。曾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IJWMIP执行主编。

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