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A Robust Fuzzy Clustering Approach and its Application to Principal Component Analysis


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Abstract

A robust fuzzy clustering approach is proposed to simplify the task of principal component analysis (PCA) by reducing the data complexity of an image. This approach performs well on function curves and character images that not only have loops, shazp corners and intersections but also include data with noise and outliers. The proposed approach is composed of two phases: fustly, input data are clustered using the proposed distance analysis to get good and reasonable number of clusters; secondly, the input data are further re-clustered by the proposed robust fuzzy c-means (RFCM) to mitigate the influence of noise and outlier data so that a good result of principal components can be found. Experimental results have shown the approach works well on PCA for both curves and images despite their input data sets include loops, corners, intersections, noise and outliers.


Keywords


Pages

Total Pages: 11
Pages: 1-11

DOI
10.1080/10798587.2011.10643149


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Published

Volume: 17
Issue: 3
Year: 2011

Cite this document


References

J. C. Bezdek, Pattern Recognition With Fuzzy Objective Function Algorithms. New York: Plenum Press, 1981.

De la Torre, F., and M.J. Black. "Robust Principal Component Analysis for Computer Vision." Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 n. pag. Crossref. Web. https://doi.org/10.1109/ICCV.2001.937541

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Y. K Yang, H. L. Shieh, and C. N. Lee, “Constructing a Fuzzy Clustering Model Based on its Data Distribution,” International Conference on Computational Intelligence for Modeling, Control and Automation (CIMCA 2004), Gold Coast, Australia, 2004.

T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and Its Application to Modeling and Control.” IEEE Transactions on Systems, Man and Cybernetics, 15(1), pp 116–132, 1985.

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

TWO YEAR CITATIONS PER DOCUMENT (SJR DATA): 0.993 (2018)
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."





Journal: 1995-Present


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