Remote Multilinear Compressive Learning with Adaptive Compression

Research output: Contribution to journalArticleScientificpeer-review


Multilinear Compressive Learning (MCL) is an efficient signal acquisition and learning paradigm for multidimensional signals. The level of signal compression affects the detection or classification performance of a MCL model, with higher compression rates often associated with lower inference accuracy. However, higher compression rates are more amenable to a wider range of applications, especially those that require low operating bandwidth and minimal energy consumption such as Internet-of-Things (IoT) applications. Many communication protocols provide support for adaptive data transmission to maximize the throughput and minimize energy consumption. By developing compressive sensing and learning models that can operate with an adaptive compression rate, we can maximize the informational content throughput of the whole application. In this paper, we propose a novel optimization scheme that enables such a feature for MCL models. Our proposal enables practical implementation of adaptive compressive signal acquisition and inference systems. Experimental results demonstrated that the proposed approach can significantly reduce the amount of computations required during the training phase of remote learning systems but also improve the informational content throughput via adaptive-rate sensing.

Original languageEnglish
Number of pages9
JournalIEEE Internet of Things Journal
Publication statusE-pub ahead of print - 23 Sep 2021
Publication typeA1 Journal article-refereed


  • Adaptive-Rate Data Acquisition.
  • Compressed sensing
  • Compressive Learning
  • Compressive Sensing
  • Internet of Things
  • IoT
  • Optimization
  • Sensors
  • Tensors
  • Throughput
  • Training

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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