In this work, we present theoretical guarantees for the performance of a sparse regression based unmixing implemented in the form of a Lasso optimization with non-negativity constraints.
In this work, we present theoretical guarantees for the performance of a sparse regression based unmixing implemented in the form of a Lasso optimization with non-negativity constraints. We provide a sufficient condition required for the exact recovery of the endmembers and validate it both theoretically and through experiments. In cases in which the condition is not verified, we explore the performance of sparse unmixing in relation to the exact recovery coefficient (ERC).. This work is by Yuki Itoh jointly with Mario Parente and Marco Duarte and was presented the IEEE WHISPERS, June, 2015 in Tokyo, Japan.
Remote Hyperspectral Observers Group
Department of Electrical and Computer Engineering
University of Massachusetts Amherst