Publication Abstract
Decoder-side Dimensionality Determination for Compressive-projection Principal Component Analysis of Hyperspectral Data
Li, W., & Fowler, J. E. (2011). Decoder-side Dimensionality Determination for Compressive-projection Principal Component Analysis of Hyperspectral Data. Proceedings of the International Conference on Image Processing. Brussels, Belgium. 329-332.
Abstract
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.