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Noise-insensitive discriminative subspace fuzzy clustering.

Xiaobin Zhi1, Tongjun Yu2, Longtao Bi2

  • 1School of Science, Xi' an University of Posts and Telecommunications, Xi'an, People's Republic of China.

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|February 23, 2023
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Summary

This study introduces a novel noise-insensitive discriminative subspace fuzzy clustering (NIDSFC) algorithm. It effectively handles noisy data, improving clustering performance where traditional methods fail.

Keywords:
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Area of Science:

  • Machine Learning
  • Data Mining
  • Pattern Recognition

Background:

  • Discriminative subspace clustering (DSC) leverages linear discriminant analysis (LDA) for dimensionality reduction and clustering high-dimensional data.
  • Existing DSC methods are sensitive to noise and outliers, leading to performance degradation.

Purpose of the Study:

  • To address the sensitivity of DSC to noise and outliers.
  • To develop a robust clustering algorithm for datasets containing noise and outliers.

Main Methods:

  • Introduced a noise-insensitive LDA (NILDA) by replacing Euclidean distance with an exponential non-Euclidean distance in the objective function.
  • Proposed a noise-insensitive discriminative subspace fuzzy clustering (NIDSFC) algorithm by integrating NILDA with an adaptive fuzzy c-means (AFKM) algorithm.

Main Results:

  • The proposed NIDSFC algorithm demonstrates improved effectiveness in handling datasets with noise and outliers.
  • Experimental results on benchmark datasets validate the performance of the NIDSFC algorithm.

Conclusions:

  • The NIDSFC algorithm offers a robust solution for discriminative subspace clustering in the presence of noise and outliers.
  • This approach enhances the reliability and accuracy of clustering for real-world datasets susceptible to noise.