Mean squared error vs. frame potential for unsupervised variable selection

Federico Zocco, Seán McLoone*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
189 Downloads (Pure)

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.

Original languageEnglish
Title of host publicationIntelligent Computing, Networked Control, and Their Engineering Applications - International Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, Proceedings
PublisherSpringer Verlag
Pages353-362
Number of pages10
Volume762
ISBN (Print)9789811063725
DOIs
Publication statusPublished - 23 Aug 2017
EventInternational Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017 - Nanjing, China
Duration: 22 Sep 201724 Sep 2017

Publication series

NameCommunications in Computer and Information Science
Volume762
ISSN (Print)1865-0929

Conference

ConferenceInternational Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017
Abbreviated titleICSEE 2017
CountryChina
CityNanjing
Period22/09/201724/09/2017

Keywords

  • Frame potential
  • Greedy algorithm
  • Unsupervised dimensionality reduction
  • Variable selection

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Zocco, F., & McLoone, S. (2017). Mean squared error vs. frame potential for unsupervised variable selection. In Intelligent Computing, Networked Control, and Their Engineering Applications - International Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, Proceedings (Vol. 762, pp. 353-362). (Communications in Computer and Information Science; Vol. 762). Springer Verlag. https://doi.org/10.1007/978-981-10-6373-2_36