Remote Multilinear Compressive Learning with Adaptive Compression

TL;DR

This paper introduces an adaptive compression scheme for Multilinear Compressive Learning, enhancing efficiency and throughput in remote sensing applications by dynamically adjusting data compression rates.

Contribution

It proposes a novel optimization method enabling adaptive compression in MCL models, improving computational efficiency and informational throughput.

Findings

Reduces training computation in remote learning systems

Enhances informational content throughput with adaptive sensing

Supports practical implementation of adaptive compressive systems

Abstract

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.