(May 14) Joint nonnegative matrix factorization for hybrid clustering based on content and connection structure

  • Subject
    Joint nonnegative matrix factorization for hybrid clustering based on content and connection structure
  • Date
    2018.05.14 (Mon) 14:00
  • Speaker
    Prof. Haesun Park (Georgia Institute of Technology, Atlanta, USA)
  • Place
    Wooribyul Seminar Room (B/D E3-2 , #2201 )

A hybrid method called JointNMF is presented for latent information discovery from data sets that contain both text content and connection structure information. The method jointly optimizes an integrated objective function, which is a combination the Nonnegative Matrix Factorization (NMF) objective function for handling text content and the Symmetric NMF (SymNMF) objective function for handling relation/connection information. An effective algorithm for the joint NMF objective function is proposed utilizing the block coordinate descent (BCD) framework. The proposed hybrid method simultaneously discovers content associations and related latent connections without any need for post-processing or additional clustering. It is shown that JointNMF can also be applied when the text content is associated with hypergraph edges. An additional capability is prediction of unknown connection information which is illustrated using some real world problems such as citation recommendations of papers and leader detection in organizations.


Dr. Haesun Park is a professor in the School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, U.S.A. She was elected as a SIAM Fellow in 2013 and IEEE Fellow in 2017 for her outstanding contributions in numerical computing, data analysis, and visual analytics.  She served as the director of the NSF/DHS FODAVA-Lead (Foundations of Data and Visual Analytics) Center 2008-2014 and the Executive Director of Center for Data Analytics 2013-2015 at Georgia Tech. She has published over 200 research papers in the areas of numerical computing, large-scale data analysis, visual analytics, text mining, and parallel computing. She has served on a large number of program committees of international conferences, workshops, advisory boards, e.g. as the conference co-chair for SIAM International Conference on Data Mining in 2008 and 2009 and an editorial board member of the leading journals such as SIAM Journal on Matrix Analysis and Applications, SIAM Journal on Scientific Computing, and IEEE Transactions on Pattern Analysis and Machine Intelligence. She gave numerous plenary keynote lectures at international meetings including those at the SIAM Conference on Applied Linear Algebra in 1997 and 2015, and SIAM International Conference on Data Mining in 2011. Before joining Georgia Tech, she was a professor in Department of Computer Science and Engineering, University of Minnesota, Twin Cities 1987- 2005 and a program director in the Computing and Communication Foundations Division at the National Science Foundation, Arlington, VA, U.S.A., 2003 - 2005. She received a Ph.D. and an M.S. in Computer Science from Cornell University, Ithaca, NY in 1987 and 1985, respectively, and a B.S. in Mathematics from Seoul National University, Seoul, Korea in 1981 with the Presidential Medal for the top graduate.

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