Decoder-side dimensionality determination for compressive-projection principal component analysis of hyperspectral data

Compressive-projection principal component analysis reconstructs vectors from random projections by recovering an approximation to the principal eigenvectors of the principal-component transform. A heuristic for the number of eigenvectors to approximate is developed to provide consistency with the Johnson-Lindenstrauss lemma and the restricted isometry property from compressed-sensing theory. The resulting heuristic is driven by only quantities known at the reconstruction side of the system. The heuristic is evaluated empirically for hyperspectral imagery and is demonstrated to provide near-optimal reconstruction quality.