Software Listing of Author : "Angshul Majumdar"

SC - Sparse Classifier FSC - Fast Sparse Classifier GSC - Group Sparse Classifier FGSC - Fast Group Sparse Classifier NSC - Nearest Subspace Classifier Requires SPGL1 - http://www.cs.ubc.ca/labs/scl/spgl1/ Requires Sparsify - http://www.see.ed.ac.uk/~tblumens/sparsify/sparsify.html Requires GroupSparseBox - http://www.mathworks.com/matlabcentral/fileexchange/22771

  • Platform: Matlab, Scripts

This is a generalized version of Principal Component Pursuit (PCP) where the sparsity is assumed in a transform domain and not in measurement domain. Moreover the samples obtained are lower dimensional projections. Inputs y - observation (lower dimensional projections) F - projection from signal domain to observation domain W - transform where the signal is sparse beta - term balancing sparsity and rank deficiency Outputs S - sparse component L - low rank component requires sparco for defining operators http://www.cs.ubc.ca/labs/scl/sparco/

  • Platform: Matlab, Scripts

Group and Block Sparse Signal reconstruction via Matching Pursuit - BMP, GMP Gradient Pursuit - block_gp, group_gp Nearly Orthogonal Matching Pursuit - block_nomp, group_nomp Partial Conjugate Gradient Pursuit - block_pcgp, group_pcgp Orthogonal Least Squares - BOLS, GOLS

  • Platform: Matlab, Scripts

Group and Block Sparse Signal reconstruction via Matching Pursuit - BMP, GMP Gradient Pursuit - block_gp, group_gp Nearly Orthogonal Matching Pursuit - block_nomp, group_nomp Partial Conjugate Gradient Pursuit - block_pcgp, group_pcgp

  • Platform: Matlab, Scripts

Group and Block Sparse Signal reconstruction via Matching Pursuit - BMP, GMP Gradient Pursuit - block_gp, group_gp Nearly Orthogonal Matching Pursuit - block_nomp, group_nomp Partial Conjugate Gradient Pursuit - block_pcgp, group_pcgp Orthogonal Least Squares - BOLS, GOLS

  • Platform: Matlab, Scripts

Contains three matrix completion algorithms and a demo script for running them. Also compares against other matrix completion algorithms - Singular Value Thresholding and Fixed Point Iteration. Solves the following three optimization problems: min rank(X) subject to ||y - M(X)||_2<err via Iterated Hard Thresholding min nuclear-norm(X) subject to ||y - M(X)||_2<err via Iterated Soft Thresholding min ||S||_p subject to ||y - M(X)||_2<err, where S = svd(X) via Iterated Soft Thresholding Requires Sparco since the masking operator has been defined in according to the Sparco framework. http://www.cs.ubc.ca/labs/scl/sparco/ The algorithms are general enough to work with any other linear operator, and not only the masking operator. The masking operator is just a special case when the problem boils down to one of matrix completion. For...

  • Platform: Matlab, Scripts

Non Convex Optimization Algorithms for Group Sparsity Solves a dummy OFDM sparse channel estimation problem Reweighted Lm,p algorithm for noiseless case min||x||_m,p s.t. y = Ax Reweighted Lm,p algorithm for noisy case min||x||_2,p s.t. ||y - Ax||_q Smoothed L2,0 algorithm solves a smooth version of min||x||_2,0 s.t. y = Ax Reweigted Lm,p is an extension of the Lp algorithm proposed in: Rick Chartrand and Wotao Yin, "Iteratively reweighted algorithms for compressive sensing", in 33rd International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2008 Smoothed L2,0 is the group version of the SL0 algorithm: Hossein Mohimani, Massoud Babaie-Zadeh, Christian Jutten, "A fast approach for overcomplete sparse decomposition based on smoothed L0 norm", IEEE Transactions on Signal Processing, Vol.57, No.1, January

  • Platform: Matlab, Scripts

It can be used in image recovery actions, image processing tasks, in signal processing, in simulations or optimization tasks.

  • Platform: Matlab, Scripts

LpAnanlysis Algorithm for solving problems of the form: min ||Ax||_p s.t. ||y-Hx||_2 < err LpSynthesis Algorithm for solving problems of the form: min ||x||_p s.t. ||y-Hx||_2 < err LpplusTV min ||x||_p + TVlambda*TV(A'x) s.t. ||y-Hx||_2 < err All of them are efficient first-order algorithms. They do not have restrictions on the operators H or A.

  • Platform: Matlab, Scripts

OLS - Orthogonal Least Squares: Proposed by T. Blumensath, M. E. Davies StOLS - Stagewise OLS: Combining StOMP ideas with OLS ROLS - Regularized OLS: Combining ROMP ideas with OLS

  • Platform: Matlab, Scripts

Constrained and Unconstrained, Analysis and Synthesis Prior Solvers for Jointly sparse Multiple Measurement Vectors. Sparco is required for running the Matlab files. Download it from http://www.cs.ubc.ca/labs/scl/sparco/ ans install it in Matlab path.

  • Platform: Matlab, Scripts
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