Multilinear Compressive Learning

Dat Thanh Tran, Mehmet Yamaç, Aysen Degerli, Moncef Gabbouj, Alexandros Iosifidis

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)


Compressive learning (CL) is an emerging topic that combines signal acquisition via compressive sensing (CS) and machine learning to perform inference tasks directly on a small number of measurements. Many data modalities naturally have a multidimensional or tensorial format, with each dimension or tensor mode representing different features such as the spatial and temporal information in video sequences or the spatial and spectral information in hyperspectral images. However, in existing CL frameworks, the CS component utilizes either random or learned linear projection on the vectorized signal to perform signal acquisition, thus discarding the multidimensional structure of the signals. In this article, we propose multilinear CL (MCL), a framework that takes into account the tensorial nature of multidimensional signals in the acquisition step and builds the subsequent inference model on the structurally sensed measurements. Our theoretical complexity analysis shows that the proposed framework is more efficient compared to its vector-based counterpart in both memory and computation requirement. With extensive experiments, we also empirically show that our MCL framework outperforms the vector-based framework in object classification and face recognition tasks, and scales favorably when the dimensionalities of the original signals increase, making it highly efficient for high-dimensional multidimensional signals.

Original languageEnglish
Article number9070152
Pages (from-to)1512-1524
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number4
Publication statusPublished - Apr 2021
MoE publication typeA1 Journal article-refereed


  • Compressive learning (CL)
  • compressive sensing (CS)
  • end-to-end learning
  • multilinear compressive sensing


Dive into the research topics of 'Multilinear Compressive Learning'. Together they form a unique fingerprint.

Cite this