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  • NN_FUFE

    Rights statement: This is the author’s version of a work that was accepted for publication in Neural Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neural Networks, 115, 2019 DOI: 10.1016/j.neunet.2019.03.008

    Accepted author manuscript, 956 KB, PDF-document

    Embargo ends: 27/03/20

    Available under license: CC BY-NC-ND

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Flexible unsupervised feature extraction for image classification

Research output: Contribution to journalJournal article

Published
  • Y. Liu
  • F. Nie
  • Q. Gao
  • X. Gao
  • J. Han
  • L. Shao
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<mark>Journal publication date</mark>1/07/2019
<mark>Journal</mark>Neural Networks
Volume115
Number of pages7
Pages (from-to)65-71
Publication statusPublished
Early online date27/03/19
Original languageEnglish

Abstract

Dimensionality reduction is one of the fundamental and important topics in the fields of pattern recognition and machine learning. However, most existing dimensionality reduction methods aim to seek a projection matrix W such that the projection W T x is exactly equal to the true low-dimensional representation. In practice, this constraint is too rigid to well capture the geometric structure of data. To tackle this problem, we relax this constraint but use an elastic one on the projection with the aim to reveal the geometric structure of data. Based on this context, we propose an unsupervised dimensionality reduction model named flexible unsupervised feature extraction (FUFE) for image classification. Moreover, we theoretically prove that PCA and LPP, which are two of the most representative unsupervised dimensionality reduction models, are special cases of FUFE, and propose a non-iterative algorithm to solve it. Experiments on five real-world image databases show the effectiveness of the proposed model.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Neural Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neural Networks, 115, 2019 DOI: 10.1016/j.neunet.2019.03.008