Title page for etd-0727118-143310


[Back to Results | New Search]

URN etd-0727118-143310
Author Wan-Chen Yang
Author's Email Address No Public.
Statistics This thesis had been viewed 5341 times. Download 0 times.
Department Information Management
Year 2017
Semester 2
Degree Master
Type of Document
Language English
Title Topic Evolution Discovery based on Regularized Matrix Factorization
Date of Defense 2018-07-20
Page Count 43
Keyword
  • Topic model
  • Hierarchical model
  • Non-negative matrix factorization
  • Hierarchical Non-negative matrix factorization
  • Topic classification
  • Topic evolution discovery
  • Multi-layer topic
  • Abstract In this thesis, we propose Hierarchical Non-negative Matrix Factorization(hNMF) to discover the hierarchical correlation among topics. The topics in lower layers are more concrete, whereas ones in higher layers are more abstract. Furthermore, our model could discover the evolution of topics, recognizing whether topics arise, merge or disappear. In our experiment, we applied nonnegative matrix factorization(NMF) on document-term matrix in each layer, detecting the evolution of topics in several years. We visualized the relationships by graphical model such as networks and trees. Topic networks present the communication between topics and terms, and topic trees reveal the hierarchical relationship among topics.
    Advisory Committee
  • Keng-Pei Lin - chair
  • Pei-Ju Lee - co-chair
  • Yihuang Kang - advisor
  • Files
  • etd-0727118-143310.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2018-08-27

    [Back to Results | New Search]


    Browse | Search All Available ETDs

    If you have more questions or technical problems, please contact eThesys