陈松灿教授学术报告
Title: High-Rankness of Missing Multi-label Learning
Speaker: Prof. Dr. Songcan Chen(陈松灿)
Affiliation: Nanjing University of Aeronautics and Astronautics
Time: 9:30-10:30am, Thursday, November 25th, 2021
Venue: Academic Hall, 1st Floor, Nanjing Center for Applied Mathematics
4 Liye Zone, TusCity, 26 Zhishi Rd, Chi-Lin Innovation Park, Nanjing
Abstract
Multi-label learning (MLL) is an important learning paradigm in machine learning where completing those missing labels is a key. One of Popular MLLs works under the low-rankness assumption for label completion, however, this is often violated in practice. In this talk, we first illustrate the high-rankness in single/multiview MLLs and more challenges in the latter setting, then provide the concise yet effective methods to meet them.
About the Speaker
陈松灿，南京航空航天大学计算机科学和技术学院/人工智能学院教授。国际模式识别学会会士(IAPR Fellow)和中国人工智能学会会士(CAAI Fellow)。Google Scholar被引16200多次，H-指数58。2014-2020连续7年入选Elsevier中国高引学者榜。现任中国人工智能学会(CAAI)常务理事，CAAI机器学习专委会主任和江苏省人工智能学会(JSAI)常务副理事长。至今主持国家自然科学基金项目12项，其中重点项目1项。