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Neurodynamics-Based Nonnegative Matrix Factorization for Classification

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11302))

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Abstract

This paper contributes to study the influence of various NMF algorithms on the classification accuracy of each classifier as well as to compare the classifiers among themselves. We focus on a fast nonnegative matrix factorization (NMF) algorithm based on a discrete-time projection neural network (DTPNN). The NMF algorithm is combined with three classifiers in order to find out the influence of dimensionality reduction performed by the NMF algorithm on the accuracy rate of the classifiers. The convergent objective function values in terms of two popular objective functions, Frobenius norm and Kullback-Leibler (K-L) divergence for different NMF based algorithms on a wide range of data sets are demonstrated. The CPU running time in terms of these objective functions on different combination of NMF algorithms and data sets are also shown. Moreover, the convergent behaviors of different NMF methods are illustrated. In order to test its effectiveness on classification accuracy, a performance study of three well-known classifiers is carried out and the influence of the NMF algorithm on the accuracy is evaluated.

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Acknowledgements

This work was supported in part by the National Science Foundation (NSF) under Grants HRD #1505509, HRD #1533479, and DUE #1654474.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of the District of Columbia, Washington, D.C., 20008, USA

    Nian Zhang & Keenan Leatham

Authors
  1. Nian Zhang
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  2. Keenan Leatham
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Corresponding author

Correspondence to Nian Zhang .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Zhang, N., Leatham, K. (2018). Neurodynamics-Based Nonnegative Matrix Factorization for Classification. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_46

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  • DOI: https://doi.org/10.1007/978-3-030-04179-3_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04178-6

  • Online ISBN: 978-3-030-04179-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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