A strategy for feature extraction of high dimensional noisy data

Document Type

Conference Proceeding

Publication Date

12-1-2006

Abstract

This paper proposes a strategy for feature extraction of noisy high dimensional data. Firstly, the moving median filter is used for reducing the effect of noise and outliers in the measurement data. Then, the data is projected to a lower dimension feature space using radial basis function (RBF) network and polygonal line (PL). A case study based on a simulated continuos stirred tank reactor (CSTR) has been investigated to check the effectiveness of the proposed strategy. The result shows that it is very effective for dimensionality reduction with minimum loss of information.

Publication Source (Journal or Book title)

Proceedings of the IASTED International Conference on Modelling, Identification, and Control, MIC

First Page

441

Last Page

445

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