  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.

Computer Vision and Pattern Recognition;Image and Video Processing