Feature Extraction of Concepts by Independent Component Analysis


Altangerel Chagnaa, Cheol-Young Ock, Chang-Beom Lee, Purev Jaimai, Journal of Information Processing Systems Vol. 3, No. 1, pp. 33-37, Feb. 2007  


Keywords: Independent Component Analysis, Clustering, Latent Concepts.
Fulltext:

Abstract

Semantic clustering is important to various fields in the modern 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.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Altangerel Chagnaa, Cheol-Young Ock, Chang-Beom Lee, & Purev Jaimai (2007). Feature Extraction of Concepts by Independent Component Analysis. Journal of Information Processing Systems, 3(1), 33-37. DOI: .

[IEEE Style]
A. Chagnaa, C. Ock, C. Lee and P. Jaimai, "Feature Extraction of Concepts by Independent Component Analysis," Journal of Information Processing Systems, vol. 3, no. 1, pp. 33-37, 2007. DOI: .

[ACM Style]
Altangerel Chagnaa, Cheol-Young Ock, Chang-Beom Lee, and Purev Jaimai. 2007. Feature Extraction of Concepts by Independent Component Analysis. Journal of Information Processing Systems, 3, 1, (2007), 33-37. DOI: .