H.-L. Shieh, Y.-K. Yang, and C.-N. Lee (Taiwan)
principal component analysis, feature extraction, smoothcurve, clustering algorithm, robust fuzzy c-means, functionapproximation.
In this paper, a new robust approach is proposed for better performing Principal component analysis (PCA) on function curves and character images that not only have sharp corners and intersections but also are bound of noise and outlier data. The proposed method is composed of two phases: firstly, input data are clustered using the proposed distance analysis to get good initial cluster centers and reasonable number of clusters; secondly, the input data are reclustered by the proposed robust fuzzy c-means (RFCM) based on the results obtained in the first phase to overcome the influence of noise and outlier data so that a good result of principal components can be found. Experimental results have demonstrated the good performance of the proposed approach.
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