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, Jun. 2007  

https://doi.org/
Keywords: Independent Component Analysis, Clustering, Latent Concepts.
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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.


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Cite this article
[APA Style]
Chagnaa, A., Ock, C., Lee, C., & Jaimai, P. (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, 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: .