Performance Indicator in Multilinear Compressive Learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Recently, the Multilinear Compressive Learning (MCL) framework was proposed to efficiently optimize the sensing and learning steps when working with multidimensional signals, i.e. tensors. In Compressive Learning in general, and in MCL in particular, the number of compressed measurements captured by a compressive sensing device characterizes the storage requirement or the bandwidth requirement for transmission. This number, however, does not completely characterize the learning performance of a MCL system. In this paper, we analyze the relationship between the input signal resolution, the number of compressed measurements and the learning performance of MCL. Our empirical analysis shows that the reconstruction error obtained at the initialization step of MCL strongly correlates with the learning performance, thus can act as a good indicator to efficiently characterize learning performances obtained from different sensor configurations without optimizing the entire system.
Original languageEnglish
Title of host publication2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Number of pages7
ISBN (Electronic)978-1-7281-2547-3
Publication statusPublished - 1 Dec 2020
Publication typeA4 Article in conference proceedings
EventIEEE Symposium Series on Computational Intelligence - Canberra, Australia
Duration: 1 Dec 20204 Dec 2020


ConferenceIEEE Symposium Series on Computational Intelligence


  • Performance evaluation
  • Sensors
  • Signal resolution
  • Robot sensing systems
  • Optimization
  • Tensors
  • Task analysis

Publication forum classification

  • Publication forum level 1


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