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International Conference on Intelligent Computing for Sustainable Energy and Environment

International Conference on Life System Modeling and Simulation

ICSEE 2017, LSMS 2017: Intelligent Computing, Networked Control, and Their Engineering Applications pp 353–362Cite as

Mean Squared Error vs. Frame Potential for Unsupervised Variable Selection

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 762))

Abstract

Forward Selection Component Analysis (FSCA) provides a pragmatic solution to the NP-hard unsupervised variable selection problem, but is not guaranteed to be optimal due to the multi-modal nature of the mean squared error (MSE) selection metric used. Frame potential (FP) is a metric that has recently been shown to yield near-optimal greedy sensor selection performance for linear inverse problems. This paper explores if FP offers similar benefits in the unsupervised variable selection context. In addition, the backward elimination counterpart of FSCA is introduced for the first time (BECA) and compared with forward and backward FP based variable selection on a number of simulated and real world datasets. It is concluded that FP does not improve on FSCA and that while BECA yields comparable results to FSCA it is not a competitive alternative due to its much higher computational complexity.

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Acknowledgments

The first author gratefully acknowledges Irish Manufacturing Research (IMR) for its financial support of his PhD.

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Authors and Affiliations

  1. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, Northern Ireland

    Federico Zocco & Seán McLoone

Authors
  1. Federico Zocco
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  2. Seán McLoone
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Corresponding author

Correspondence to Seán McLoone .

Editor information

Editors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, China

    Dong Yue

  2. Shanghai University , Shanghai, China

    Chen Peng

  3. Shanghai University , Shanghai, China

    Dajun Du

  4. Nanjing University of Posts and Telecommunications, Nanjing, China

    Tengfei Zhang

  5. Shanghai University , Shanghai, China

    Min Zheng

  6. Swinburne University of Technology, Melbourne, Victoria, Australia

    Qinglong Han

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© 2017 Springer Nature Singapore Pte Ltd.

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Zocco, F., McLoone, S. (2017). Mean Squared Error vs. Frame Potential for Unsupervised Variable Selection. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_36

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  • DOI: https://doi.org/10.1007/978-981-10-6373-2_36

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6372-5

  • Online ISBN: 978-981-10-6373-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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