On the Strong Convergence of Forward-Backward Splitting in Reconstructing Jointly Sparse Signals
Sign Up to like & getrecommendations! Published in 2021 at "Set-Valued and Variational Analysis"
DOI: 10.1007/s11228-021-00603-2
Abstract: We consider the problem of reconstructing a set of sparse vectors sharing a common sparsity pattern from incomplete measurements. To take account of the joint sparsity and promote the coupling of nonvanishing components, we employ… read more here.
Keywords: convergence forward; forward backward; backward splitting; jointly sparse ... See more keywords
Urban Land Cover Mapping from Airborne Hyperspectral Imagery Using a Fast Jointly Sparse Spectral Mixture Analysis Method
Sign Up to like & getrecommendations! Published in 2020 at "Canadian Journal of Remote Sensing"
DOI: 10.1080/07038992.2020.1791693
Abstract: Abstract Due to the fragmented compositional structure of urban scenes, many pixels are mixtures of multiple materials even in high spatial resolution airborne hyperspectral data. In the past ten years, sparse regression based spectral unmixing… read more here.
Keywords: spectral mixture; mixture analysis; jointly sparse; method ... See more keywords
Efficient and Robust Distributed Digital Codec Framework for Jointly Sparse Correlated Signals
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2920982
Abstract: In this paper, we propose a novel distributed digital transmission framework for two jointly sparse correlated signals. First, the non-zero coefficients of each signal are quantized by a standard quantizer or a novel distributed quantizer,… read more here.
Keywords: compressed sensing; correlated signals; jointly sparse; sparse correlated ... See more keywords
Generalized Robust Regression for Jointly Sparse Subspace Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2018.2812802
Abstract: Ridge regression is widely used in multiple variable data analysis. However, in very high-dimensional cases such as image feature extraction and recognition, conventional ridge regression or its extensions have the small-class problem, that is, the… read more here.
Keywords: regression; robust regression; generalized robust; subspace learning ... See more keywords
Jointly Sparse Locality Regression for Image Feature Extraction
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Multimedia"
DOI: 10.1109/tmm.2019.2961508
Abstract: This paper proposes a novel method called Jointly Sparse Locality Regression (JSLR) for feature extraction and selection. JSLR utilizes joint $L_{2,1}$-norm minimization on regularization term, and also introduces the locality to characterize the local geometric… read more here.
Keywords: regression; feature extraction; feature; locality ... See more keywords