Semisupervised Nonnegative Matrix Factorization for learning the semantics

Bin Shen, Zhanibek Datbayev, Olzhas Makhambetov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In real world there are a lot of unlabeled data, and relatively few labeled data. Unlabeled data help to learn a statistical model that can fully describe the global property of data, while labeled data help to minimize the gap between the statistical property and human beings' perception, i.e. labeled data can help to learn the semantics. Nonnegative Matrix Factorization is a popular technique in data analysis, since a lot of real world data are nonnegative. However, traditional NMF is an unsupervised learning algorithm, which means that it cannot make use of the label information. To enable NMF to make use of both labeled and unlabeled data samples, we propose a novel semisupervised Nonnegative Matrix Factorization technique for learning the semantics. The proposed algorithm extracts prior information from the labeled data, and then uses it to guide the later processing. Experimental results with different settings prove the efficacy of the proposed algorithm.

Original languageEnglish
Title of host publication6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012
Pages821-824
Number of pages4
DOIs
Publication statusPublished - Dec 1 2012
Event2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012 - Kobe, Japan
Duration: Nov 20 2012Nov 24 2012

Publication series

Name6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012

Other

Other2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012
CountryJapan
CityKobe
Period11/20/1211/24/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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