报告题目/Title: Generalized two-dimensional linear discriminant analysis with regularization
报告人/Speaker: 邵元海 教授（海南大学）
时间/Date & Time: 2018年11月5 日15:30-16:30
Recent advances show that two-dimensional linear discriminant analysis (2DLDA) is a successful matrix-based dimensionality reduction method. However, 2DLDA may encounter the singularity issue theoretically and the sensitivity to outliers. In this paper, a generalized Lp-norm 2DLDA framework with regularization for an arbitrary p>0 is proposed, named G2DLDA. There are mainly two contributions of G2DLDA: one is G2DLDA model uses an arbitrary Lp-norm to measure the between-class and within-class scatter, and hence a proper p can be selected to achieve the robustness. The other one is that by introducing an extra regularization term, G2DLDA achieves better generalization performance, and solves the singularity problem. In addition, G2DLDA can be solved through a series of convex problems with equality constraint, and it has closed solution for each single problem. Its convergence can be guaranteed theoretically when 1≤p≤2. Preliminary experimental results on three contaminated human face databases show the effectiveness of the proposed G2DLDA.
报告人简介/About the speaker:
邵元海, 1983年04月生, 海南大学校聘教授，硕士生导师.主要从事数据挖掘、机器学习，以及最优化算法和应用研究. 主持国家自然科学基金青年基金项目3项, 省自然科学基金项目3项, 浙江省中青年学科带头人学术攀登项目1项. 发表科研论文100余篇, 其中SCI期刊论文40余篇, 论文引用率（google学术）1600余次. 兼任国家自然科学基金项目匿名评审专家,教育部学位中心通讯评议专家,中国统计学会理事,中国运筹学会会员等. 国际会议：International Conference on Machine Learning and Signal Processing 国际指导委员会委员, IEEE International Conference on Data Mining Workshops 程序委员会委员, International Conference on Information Technology and Quantitative Management 程序委员会委员,被邀请给IEEE TNNLS, IEEE TFS, Journal of Global Optimization, Knowledge and Information Systems等30 余期刊和国内外会议审稿.