Modified kernel-based nonlinear feature extraction.

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Feature Extraction (FE) techniques are widely used in many applications to pre-process data in order to reduce the complexity of subsequent processes. A group of Kernel-based nonlinear FE ( H E ) algorithms has attracted much attention due to their high performance. However, a serious limitation that is inherent in these algorithms -- the maximal number of features extracted by them is limited by the number of classes involved -- dramatically degrades their flexibility. Here we propose a modified version of those KFE algorithms (MKFE), This algorithm is developed from a special form of scatter-matrix, whose rank is not determined ... continued below

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6 p.

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Ma, J. (Junshui); Perkins, S. J. (Simon J.); Theiler, J. P. (James P.) & Ahalt, S. (Stanley) January 1, 2002.

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Description

Feature Extraction (FE) techniques are widely used in many applications to pre-process data in order to reduce the complexity of subsequent processes. A group of Kernel-based nonlinear FE ( H E ) algorithms has attracted much attention due to their high performance. However, a serious limitation that is inherent in these algorithms -- the maximal number of features extracted by them is limited by the number of classes involved -- dramatically degrades their flexibility. Here we propose a modified version of those KFE algorithms (MKFE), This algorithm is developed from a special form of scatter-matrix, whose rank is not determined by the number of classes involved, and thus breaks the inherent limitation in those KFE algorithms. Experimental results suggest that MKFE algorithm is .especially useful when the training set is small.

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6 p.

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  • Submitted to: International Conference on Machine Learning and Applications 2002

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  • Report No.: LA-UR-02-0292
  • Report No.: LA-UR-02-292
  • Grant Number: none
  • Office of Scientific & Technical Information Report Number: 975954
  • Archival Resource Key: ark:/67531/metadc929570

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Office of Scientific & Technical Information Technical Reports

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Creation Date

  • January 1, 2002

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Dec. 12, 2016, 12:25 p.m.

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Ma, J. (Junshui); Perkins, S. J. (Simon J.); Theiler, J. P. (James P.) & Ahalt, S. (Stanley). Modified kernel-based nonlinear feature extraction., article, January 1, 2002; United States. (digital.library.unt.edu/ark:/67531/metadc929570/: accessed December 13, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.