Home > Research > Publications & Outputs > Flexible unsupervised feature extraction for im...

Electronic data

  • 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, 957 KB, PDF document

    Available under license: CC BY-NC-ND

Links

Text available via DOI:

View graph of relations

Flexible unsupervised feature extraction for image classification

Research output: Contribution to journalJournal article

Published

Standard

Flexible unsupervised feature extraction for image classification. / Liu, Y.; Nie, F.; Gao, Q.; Gao, X.; Han, J.; Shao, L.

In: Neural Networks, Vol. 115, 01.07.2019, p. 65-71.

Research output: Contribution to journalJournal article

Harvard

Liu, Y, Nie, F, Gao, Q, Gao, X, Han, J & Shao, L 2019, 'Flexible unsupervised feature extraction for image classification', Neural Networks, vol. 115, pp. 65-71. https://doi.org/10.1016/j.neunet.2019.03.008

APA

Liu, Y., Nie, F., Gao, Q., Gao, X., Han, J., & Shao, L. (2019). Flexible unsupervised feature extraction for image classification. Neural Networks, 115, 65-71. https://doi.org/10.1016/j.neunet.2019.03.008

Vancouver

Author

Liu, Y. ; Nie, F. ; Gao, Q. ; Gao, X. ; Han, J. ; Shao, L. / Flexible unsupervised feature extraction for image classification. In: Neural Networks. 2019 ; Vol. 115. pp. 65-71.

Bibtex

@article{80b1ee2202ef413d97d5a0e32e8422fd,
title = "Flexible unsupervised feature extraction for image classification",
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. ",
keywords = "Dimensionality reduction, Feature extraction, Unsupervised, Extraction, Geometry, Iterative methods, Learning systems, Dimensionality reduction method, Dimensionality-reduction models, Geometric structure, Low-dimensional representation, Non-iterative algorithms, Projection matrix, Image classification, article, feature extraction, theoretical study",
author = "Y. Liu and F. Nie and Q. Gao and X. Gao and J. Han and L. Shao",
note = "This is the author{\textquoteright}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 ",
year = "2019",
month = jul,
day = "1",
doi = "10.1016/j.neunet.2019.03.008",
language = "English",
volume = "115",
pages = "65--71",
journal = "Neural Networks",
issn = "0893-6080",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - Flexible unsupervised feature extraction for image classification

AU - Liu, Y.

AU - Nie, F.

AU - Gao, Q.

AU - Gao, X.

AU - Han, J.

AU - Shao, L.

N1 - 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

PY - 2019/7/1

Y1 - 2019/7/1

N2 - 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.

AB - 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.

KW - Dimensionality reduction

KW - Feature extraction

KW - Unsupervised

KW - Extraction

KW - Geometry

KW - Iterative methods

KW - Learning systems

KW - Dimensionality reduction method

KW - Dimensionality-reduction models

KW - Geometric structure

KW - Low-dimensional representation

KW - Non-iterative algorithms

KW - Projection matrix

KW - Image classification

KW - article

KW - feature extraction

KW - theoretical study

U2 - 10.1016/j.neunet.2019.03.008

DO - 10.1016/j.neunet.2019.03.008

M3 - Journal article

VL - 115

SP - 65

EP - 71

JO - Neural Networks

JF - Neural Networks

SN - 0893-6080

ER -