Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Feature Extraction of Concepts by Independent Component Analysis

  • Chagnaa, Altangerel (School of Computer Engineering and Information Technology, University of Ulsan) ;
  • Ock, Cheol-Young (School of Computer Engineering and Information Technology, University of Ulsan) ;
  • Lee, Chang-Beom (Investigation & Analysis Team, Korean Transportation Safety Agency) ;
  • Jaimai, Purev (School of Information Technology, National University of Mongolia)
  • Published : 2007.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Semantic clustering is important to various fields in the modem information society. In this work we applied the Independent Component Analysis method to the extraction of the features of latent concepts. We used verb and object noun information and formulated a concept as a linear combination of verbs. The proposed method is shown to be suitable for our framework and it performs better than a hierarchical clustering in latent semantic space for finding out invisible information from the data.

Keywords

References

  1. Hyvarinen, A., Karhunen, J. and Oja, E., 'Independent Component Analysis', John Wiley & Sons, 2001
  2. Bigham, E., Kuusisto, J., and Lagus, K., 'ICA and SOM in Text Document Analysis', Proceedings of the 25th ACM SIGIR 2002 International Conference on Research and Development in Information Retrieval, Tampere, Finland, pp. 361-362, 2002 https://doi.org/10.1145/564376.564444
  3. Kolenda, T., Hansen, L., and Sigurdsan, S., 'Independent Components in Text', In M. Girolami, editor, Advances in Independent Component Analysis, Springer-Verlag, pp. 235-256, 2000
  4. Honkela, T., Hyvarinen, A., and Vayrynen, J., 'Emergence of Linguistic Features: Independent Analysis of Contexts', In Proc. of the Neural Computation and Psychology Workshop 9, Plymouth, UK,2005
  5. Church, K., and Hanks, P., 'Word Association Norms, Mutual Information and Lexicography', Computational Linguistics, vol. 16, pp. 22-29, 1990
  6. Manning, C, and Schutze, H., 'Foundations of Statistical Natural Language Processing', Cambridge, MA: MIT Press, 1999
  7. Hyvarinen, A. 'Fast and Robust Fixed-Point Algorithms for Independent Component Analysis', IEEE Tr. on Neural Networks, Vol. 10, No.3, pp. 626-634, 1999 https://doi.org/10.1109/72.761722
  8. Scott C. Deerwester et aI., 'Indexing by Latent Semantic Analysis', Journal of the American Society of Information Science, no. 41, pp. 391-407, 1990 https://doi.org/10.1002/(SICI)1097-4571

Cited by

  1. Improving clustering performance using independent component analysis and unsupervised feature learning vol.8, pp.1, 2018, https://doi.org/10.1186/s13673-018-0148-3