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Toward an efficient and accurate unsupervised feature selection

Toward an efficient and accurate unsupervised feature selection

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Both redundant and nonrepresentative features result in large-volume and high-dimensional data, which degrade the accuracy and performance of classification as well as clustering algorithms. Most of the existing feature selection (FS) methods have limitations when dealing with high-dimensional data, as they search different subsets of features to find accurate representations of all features. Obviously, searching for different combinations of features is computationally very expensive, which makes existing work not efficient for high-dimensional data. The work carried out in this chapter, which relates to the design of an efficient and accurate similarity-based unsupervised feature selection (AUFS) method, tackles mainly the high-dimensionality issue of data by selecting a reduced set of representative and nonredundant features without the need for data class labels.

Chapter Contents:

  • 5.1 Introduction
  • 5.2 Related work
  • 5.2.1 Supervised feature selection methods
  • 5.2.2 Unsupervised feature selection methods
  • 5.3 Similarity measures
  • 5.4 The proposed feature selection method
  • 5.4.1 More details about the AUFS method
  • 5.4.2 An illustrative example
  • 5.5 Experimental setup
  • 5.5.1 Datasets
  • 5.5.2 Evaluation metrics
  • 5.6 Experimental results
  • 5.7 Conclusion

Inspec keywords: feature extraction; feature selection; pattern clustering; pattern classification; unsupervised learning

Other keywords: nonrepresentative features; existing feature selection methods; nonredundant features; accurate unsupervised feature selection; representative features; accurate representations; data class labels; redundant features; high-dimensional data; efficient feature selection

Subjects: Knowledge engineering techniques; Data handling techniques; Other topics in statistics; Computer vision and image processing techniques

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