Feature Extraction of Concepts by Independent Component Analysis

Altangerel Chagnaa, Cheol-Young Ock, Chang-Beom Lee and Purev Jaimai
Volume: 3, No: 1, Page: 33 ~ 37, Year: 2007

Keywords: Independent Component Analysis, Clustering, Latent Concepts.
Full Text:

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.

Article Statistics
Multiple requests among the same broswer session are counted as one view (or download).
If you mouse over a chart, a box will show the data point's value.


Cite this article
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: .