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英国伯恩茅斯大学田丰教授学术讲座: Multi-Component Nonnegative Matrix Factorization for Data Clustering

阅读数:44    发布:2019-04-16 09:51    

英国伯恩茅斯大学田丰教授学术讲座: Multi-Component Nonnegative Matrix Factorization for Data Clustering

时间:2019年4月22日(星期一)下午三点

地点:计算机与软件学院413会议室

摘要:

A good data representation can typically reveal the latent structure of data and facilitate further processes such as clustering, classification and recognition. Nonnegative matrix factorization (NMF) as a fundamental approach for data representation has attracted great attentions because it possesses parts-of-whole interpretations and produces superior practical performance. However, NMF fails to explore the semantic information of multiple components as well as the diversity among them, which would be of great benefit to understand data comprehensively and in depth. In fact, real data are usually complex and contain various components. For example, face images have ex-pressions and genders. Each component mainly reflects one aspect of data and provides information others do not have. In this talk, I will present a novel multi-component nonnegative matrix factorization (MCNMF). Instead of seeking only one representation of data, MCNMF learns multiple representations simultaneously, where each representation corresponds to a component. By integrating the multiple representations, a more comprehensive representation is then established. The experimental results on real datasets will be presented in the talk, demonstrating MCNMF’s excellent performance with the aggregated representation.

专家简介:

田丰博士目前是英国伯恩茅斯大学 (Bournemouth University, UK) 科学与技术学院的副教授。毕业于西安交通大学,田丰博士在计算机图形学,图像处理,等方面有超20年的研究经验,并在国际期刊和会议上发表超过100篇以上的学术论文, 包括IEEE TVCG, ACM TMCS等, 并取得3项国际专利。在英国工作之前,田丰博士曾先后在新加坡南洋理工大学计算机图像与图形研究中心做博士后,南洋理工大学计算机工程学院担任助理教授等。期间,他获得过来自于新加坡科技研发局,国家研究基金会,英国皇家学院,日本科学促进会, 欧盟 Horizon 2020等的多个项目基金。作为访问学者,田丰博士和法国里昂大学, 澳洲新南威尔士大学, 日本东京工业大学等有着密切和广泛的研究合作。


深圳大学计算机与软件学院 2009-2016