Vector quantization and signal compression
Vector quantization and signal compression
Matrix computations (3rd ed.)
The symmetric eigenvalue problem
The symmetric eigenvalue problem
An analysis of the Rayleigh—Ritz method for approximating eigenspaces
Mathematics of Computation
An elementary proof of a theorem of Johnson and Lindenstrauss
Random Structures & Algorithms
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
Algorithms for simultaneous sparse approximation: part I: Greedy pursuit
Signal Processing - Sparse approximations in signal and image processing
Algorithms for simultaneous sparse approximation: part II: Convex relaxation
Signal Processing - Sparse approximations in signal and image processing
Recovery Algorithms for Vector-Valued Data with Joint Sparsity Constraints
SIAM Journal on Numerical Analysis
Compressive-Projection Principal Component Analysis for the Compression of Hyperspectral Signatures
DCC '08 Proceedings of the Data Compression Conference
Low-Complexity Principal Component Analysis for Hyperspectral Image Compression
International Journal of High Performance Computing Applications
Compressive-Projection Principal Component Analysis and the First Eigenvector
DCC '09 Proceedings of the 2009 Data Compression Conference
IEEE Transactions on Signal Processing
An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem
IEEE Transactions on Signal Processing - Part II
Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Compressive acquisition of dynamic scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
CROS: A Contingency Response multi-agent system for Oil Spills situations
Applied Soft Computing
Randomized SVD methods in hyperspectral imaging
Journal of Electrical and Computer Engineering - Special issue on Algorithms for Multispectral and Hyperspectral Image Analysis
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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. A process is presented that effectively shifts the computational burden of PCA from the resource-constrained encoder to a presumably more capable base-station decoder. The proposed approach, compressive-projection PCA (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. An analysis is presented that extends existing Rayleigh-Ritz theory to the special case of highly eccentric distributions; this analysis in turn motivates a reconstruction process at the CPPCA decoder that consists of a novel eigenvector reconstruction based on a convex-set optimization driven by Ritz vectors within the projected subspaces. As such, CPPCA constitutes a fundamental departure from traditional PCA in that it permits its excellent dimensionality-reduction and compression performance to be realized in an light-encoder/heavy-decoder system architecture. In experimental results, CPPCA outperforms a multiple-vector variant of compressed sensing for the reconstruction of hyperspectral data.