报告主题：保秩非负矩阵分解

Ranking Preserving Nonnegative Matrix Factorization

报告时间： 9月20日(周五) 上午9:30

报告地点：计算机学院A411会议室

报  告 人：王晶，日本东京大学博士后研究员

    王晶，现任日本东京大学博士后，机器学习，数据挖掘方向，专攻降维，聚类，多视角学习等。2018年1月于英国伯恩茅斯大学取得博士学位，之前于香港城市大学取得硕士学位。博士期间凭借其研究成果获得全英“2017 ABTA Doctoral research award”工程自然科学类第二名。目前共发表论文近20篇，其中包括CCF-A类会议IJCAI 2019，AAAI 2019, KDD2019，IJCAI 2018, IJCAI 2017 以及 JCR-1区期刊 IEEE transactions on Cybernetics, IEEE transactions on Image Processing等。并担任国际期刊及会议审稿人，包括AAAI 2020 2019, IEEE transactions on Knowledge and Data Engineering, IEEE transactions on Image Processing等.

报告摘要:

Nonnegative matrix factorization (NMF), a wellknown technique to find parts-based representations of nonnegative data, has been widely studied. In reality, ordinal relations often exist among data, such as data i is more related to j than to q. Such relative order is naturally available, and more importantly, it truly reflects the latent data structure. Preserving the ordinal relations enables us to find structured representations of data that are faithful to the relative order, so that the learned representations become more discriminative. However, this cannot be achieved by current NMFs. In this paper, we make the first attempt towards incorporating the ordinal relations and propose a novel ranking preserving nonnegative matrix factorization (RPNMF) approach, which enforces the learned representations to be ranked according to the relations. We derive iterative updating rules to solve RPNMF’s objective function with convergence guaranteed. Experimental results with several datasets for clustering and classification have demonstrated that RPNMF achieves greater performance against the state-of-the-arts, not only in terms of accuracy, but also interpretation of orderly data structure.