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Classification using sparse representations: a biologically plausible approach

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Classification using sparse representations : a biologically plausible approach. / Spratling, Michael.

In: Biological Cybernetics, Vol. 108, No. 1, 02.2014, p. 61-73.

Research output: Contribution to journalArticlepeer-review

Harvard

Spratling, M 2014, 'Classification using sparse representations: a biologically plausible approach', Biological Cybernetics, vol. 108, no. 1, pp. 61-73. https://doi.org/10.1007/s00422-013-0579-x

APA

Spratling, M. (2014). Classification using sparse representations: a biologically plausible approach. Biological Cybernetics, 108(1), 61-73. https://doi.org/10.1007/s00422-013-0579-x

Vancouver

Spratling M. Classification using sparse representations: a biologically plausible approach. Biological Cybernetics. 2014 Feb;108(1):61-73. https://doi.org/10.1007/s00422-013-0579-x

Author

Spratling, Michael. / Classification using sparse representations : a biologically plausible approach. In: Biological Cybernetics. 2014 ; Vol. 108, No. 1. pp. 61-73.

Bibtex Download

@article{71beaf1661a64b6ea82083aa6b8c5f37,
title = "Classification using sparse representations: a biologically plausible approach",
abstract = "Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and computer vision. While this approach was originally inspired by insights into cortical information processing, biologically plausible approaches have been limited to exploring the functionality of early sensory processing in the brain, while more practical applications have employed non-biologically plausible sparse coding algorithms. Here, a biologically plausible algorithm is proposed that can be applied to practical problems. This algorithm is evaluated using standard benchmark tasks in the domain of pattern classification, and its performance is compared to a wide range of alternative algorithms that are widely used in signal and image processing. The results show that for the classification tasks performed here, the proposed method is competitive with the best of the alternative algorithms that have been evaluated. This demonstrates that classification using sparse representations can be performed in a neurally plausible manner, and hence, that this mechanism of classification might be exploited by the brain.",
author = "Michael Spratling",
year = "2014",
month = feb,
doi = "10.1007/s00422-013-0579-x",
language = "English",
volume = "108",
pages = "61--73",
journal = "Biological Cybernetics",
issn = "0340-1200",
publisher = "Springer Verlag",
number = "1",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Classification using sparse representations

T2 - a biologically plausible approach

AU - Spratling, Michael

PY - 2014/2

Y1 - 2014/2

N2 - Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and computer vision. While this approach was originally inspired by insights into cortical information processing, biologically plausible approaches have been limited to exploring the functionality of early sensory processing in the brain, while more practical applications have employed non-biologically plausible sparse coding algorithms. Here, a biologically plausible algorithm is proposed that can be applied to practical problems. This algorithm is evaluated using standard benchmark tasks in the domain of pattern classification, and its performance is compared to a wide range of alternative algorithms that are widely used in signal and image processing. The results show that for the classification tasks performed here, the proposed method is competitive with the best of the alternative algorithms that have been evaluated. This demonstrates that classification using sparse representations can be performed in a neurally plausible manner, and hence, that this mechanism of classification might be exploited by the brain.

AB - Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and computer vision. While this approach was originally inspired by insights into cortical information processing, biologically plausible approaches have been limited to exploring the functionality of early sensory processing in the brain, while more practical applications have employed non-biologically plausible sparse coding algorithms. Here, a biologically plausible algorithm is proposed that can be applied to practical problems. This algorithm is evaluated using standard benchmark tasks in the domain of pattern classification, and its performance is compared to a wide range of alternative algorithms that are widely used in signal and image processing. The results show that for the classification tasks performed here, the proposed method is competitive with the best of the alternative algorithms that have been evaluated. This demonstrates that classification using sparse representations can be performed in a neurally plausible manner, and hence, that this mechanism of classification might be exploited by the brain.

U2 - 10.1007/s00422-013-0579-x

DO - 10.1007/s00422-013-0579-x

M3 - Article

VL - 108

SP - 61

EP - 73

JO - Biological Cybernetics

JF - Biological Cybernetics

SN - 0340-1200

IS - 1

ER -

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