CPPCA—Compressive-Projection Principal Component Analysis

CPPCA—Compressive-Projection Principal Component Analysis

James E. Fowler

>About CPPCA

Principal component analysis (PCA) is often central to dimensionality reduction and compression in many applications, yet its data-dependent nature as a transform computed via expensive eigendecomposition often hinders its use in severely resource-constrained settings such as satellite-borne sensors. Compressive-projection principal component analysis (CPPCA) is a process that effectively shifts the computational burden of PCA from the resource-constrained encoder to a presumably more capable base-station decoder. CPPCA is driven by projections at the sensor onto lower-dimensional subspaces chosen at random, while the CPPCA decoder, given only these random projections, recovers not only the coefficients associated with the PCA transform, but also an approximation to the PCA transform basis itself. The reconstruction process at the CPPCA decoder consists of a novel eigenvector reconstruction based on a convex-set optimization driven by Ritz vectors within the projected subspaces.




This material is based upon work supported by the National Science Foundation under Grant No. 0915307. Any opinions, findings and conclusions, or recomendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation (NSF).

Last update: 7-oct-2014