A Strategy for Feature Extraction of High Dimensional Noisy Data

B. Bhushan (Australia) and J.A. Romagnoli (USA)


Radial basis function networks, moving median filter, feature extraction, polygonal line


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.

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