Dimension Reduction Methods on High Dimensional Streaming Data with Concept Drift


KIPS Transactions on Software and Data Engineering, Vol. 5, No. 8, pp. 361-368, Aug. 2016
10.3745/KTSDE.2016.5.8.361,   PDF Download:
Keywords: High Dimensional Streaming Data, Incremental Dimension Reduction Method, Adaptive Classifier, Concept Drift
Abstract

While dimension reduction methods on high dimensional data have been widely studied, research on dimension reduction methods for high dimensional streaming data with concept drift is limited. In this paper, we review incremental dimension reduction methods and propose a method to apply dimension reduction efficiently in order to improve classification performance on high dimensional streaming data with concept drift.


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Cite this article
[IEEE Style]
C. H. Park, "Dimension Reduction Methods on High Dimensional Streaming Data with Concept Drift," KIPS Transactions on Software and Data Engineering, vol. 5, no. 8, pp. 361-368, 2016. DOI: 10.3745/KTSDE.2016.5.8.361.

[ACM Style]
Cheong Hee Park. 2016. Dimension Reduction Methods on High Dimensional Streaming Data with Concept Drift. KIPS Transactions on Software and Data Engineering, 5, 8, (2016), 361-368. DOI: 10.3745/KTSDE.2016.5.8.361.