Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Improved FOCUSS method with conjugate gradient iterations
IEEE Transactions on Signal Processing
Blind multiband signal reconstruction: compressed sensing for analog signals
IEEE Transactions on Signal Processing
Robust recovery of signals from a structured union of subspaces
IEEE Transactions on Information Theory
Joint covariate selection and joint subspace selection for multiple classification problems
Statistics and Computing
Block-sparse signals: uncertainty relations and efficient recovery
IEEE Transactions on Signal Processing
Analysis of orthogonal matching pursuit using the restricted isometry property
IEEE Transactions on Information Theory
Detecting the Number of Clusters in n-Way Probabilistic Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved stability conditions of BOGA for noisy block-sparse signals
Signal Processing
A sparse signal reconstruction perspective for source localization with sensor arrays
IEEE Transactions on Signal Processing - Part II
Sparse solutions to linear inverse problems with multiple measurement vectors
IEEE Transactions on Signal Processing
Convolutive Blind Source Separation in the Frequency Domain Based on Sparse Representation
IEEE Transactions on Audio, Speech, and Language Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
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Recently, it has been found that the redundant blocks problem existed in many fields, such as face recognition and motion segmentation. In this paper, taking the redundant blocks into account, we propose some greedy type algorithms that exploit the subspace information of the redundant blocks to solve the redundant blocks problem. The exact recovery conditions of these algorithms are presented via block restricted isometry property (RIP). Numerical experiments demonstrate the validity of these algorithms in solving the problems with both non-redundant and redundant blocks.