On Large-Scale Dynamic Topic Modeling with Nonnegative CP Tensor Decomposition
Southern Methodist University（南卫理公会大学）；Grinnell College（格林内尔学院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Colorado State University（科罗拉多州立大学）；California State University, Channel Islands（加利福尼亚州立大学海峡群岛分校）；Occidental College（西方学院）；Boston Medical Center（波士顿医疗中心）
Miju Ahn, Nicole Eikmeier, Jamie Haddock, Lara Kassab, Alona Kryshchenko, Kathryn Leonard, Deanna Needell, R. W. M. A. Madushani, Elena Sizikova, Chuntian Wang
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23 pages, 29 figures, submitted to Women in Data Science and Mathematics (WiSDM) Workshop Proceedings, "Advances in Data Science", AWM-Springer series
There is currently an unprecedented demand for large-scale temporal data analysis due to the explosive growth of data. Dynamic topic modeling has been widely used in social and data sciences with the goal of learning latent topics that emerge, evolve, and fade over time. Previous work on dynamic topic modeling primarily employ the method of nonnegative matrix factorization (NMF), where slices of the data tensor are each factorized into the product of lower-dimensional nonnegative matrices. With this approach, however, information contained in the temporal dimension of the data is often neglected or underutilized. To overcome this issue, we propose instead adopting the method of nonnegative CANDECOMP/PARAPAC (CP) tensor decomposition (NNCPD), where the data tensor is directly decomposed into a minimal sum of outer products of nonnegative vectors, thereby preserving the temporal information. The viability of NNCPD is demonstrated through application to both synthetic and real data, where significantly improved results are obtained compared to those of typical NMF-based methods. The advantages of NNCPD over such approaches are studied and discussed. To the best of our knowledge, this is the first time that NNCPD has been utilized for the purpose of dynamic topic modeling, and our findings will be transformative for both applications and further developments.
Topic-aware chatbot using Recurrent Neural Networks and Nonnegative Matrix Factorization
Hunan University（湖南大学）；University of Vermont（佛蒙特大学）；Zhejiang University（浙江大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Harvey Mudd College（哈维穆德学院）；Shanghai Jiao Tong University（上海交通大学）；Beihang University（北京航空航天大学）
COVID-19 Literature Topic-Based Search via Hierarchical NMF
Wofford College（沃福德学院）；University of California, Los Angeles（加州大学洛杉矶分校）；California State University Channel Islands（加州州立大学海峡群岛分校）；LWS Research（LWS研究公司）
Improved SVD-based Initialization for Nonnegative Matrix Factorization using Low-Rank Correction
Graduate School of Science and Engineering（研究生院）；PAF-Karachi Institute of Economics and Technology（卡拉奇经济和技术学院）；Department of Mathematics and Operational Research（数学与运筹学系）
Semi-supervised Nonnegative Matrix Factorization for Document Classification
Harvey Mudd College（哈维穆德学院）；Colorado State Univ.（科罗拉多州立大学）；Univ. of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Cal. State Univ. Channel Islands（加州州立大学海峡群岛分校）；Wofford College（沃福德学院）；New York Univ.（纽约大学）；Univ. of Alabama（阿拉巴马大学）；Univ. of Cal., Los Angeles（加州大学洛杉矶分校）；Boston Medical Cent.（波士顿医疗中心）；Southern Methodist Univ.（南卫理公会大学）；Occidental College（西方学院）
Nonnegative Matrix Factorization through Cone Collapse
Department of Statistics（统计学系）；Wisconsin Institute of Discovery（威斯康星发现研究所）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Department of Biostatistics and Medical Informatics（生物统计学与医学信息学系）
Semi-supervised NMF Models for Topic Modeling in Learning Tasks
UCLA（加州大学洛杉矶分校）；Colorado State University（科罗拉多州立大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）；CSU Channel Islands（加州州立大学海峡群岛分校）；Wofford College（沃福德学院）；NYU（纽约大学）；University of Alabama（阿拉巴马大学）；Boston Medical Center（波士顿医疗中心）；Southern Methodist University（南卫理公会大学）；Occidental College（occidental学院）
National Institute of Informatics（国立信息学研究所）；JST, PRESTO（日本科学技术振兴机构先驱研究项目）；RIKEN Center for Brain Science（理化学研究所脑科学中心）；The University of Tokyo（东京大学）；NIMS（日本物质材料研究机构）；RIKEN AIP（理化学研究所先进智能项目中心）
Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness
Guangdong University of Technology（广东工业大学）；RIKEN Brain Science Institute（理化学研究所脑科学研究所）；Systems Research Institute, Polish Academy of Science（波兰科学院系统研究所）
Fast Learnings of Coupled Nonnegative Tensor Decomposition Using Optimal Gradient and Low-rank Approximation
Stem Cell Clinical Research Center, The First Affiliated Hospital of Dalian Medical University（大连医科大学附属第一医院干细胞临床研究中心）；Dalian Innovation Institute of Stem Cell and Precision Medicine（大连干细胞与精准医学创新研究院）；Faculty of Information Technology, University of Jyväskylä（于韦斯屈莱大学信息技术学院）；School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology（大连理工大学医学院生物医学工程学院）；Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education（教育部社会计算与认知智能重点实验室）
FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning
School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology（大连理工大学医学院生物医学工程学院）；Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province, Dalian University of Technology（大连理工大学辽宁省集成电路与生物医学电子系统重点实验室）；Stem Cell Clinical Research Center, the First Affiliated Hospital of Dalian Medical University（大连医科大学附属第一医院干细胞临床研究中心）；Dalian Innovation Institute of Stem Cell and Precision Medicine（大连干细胞与精准医学创新研究院）；Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education, China（教育部社会计算与认知智能重点实验室（大连理工大学））
