Manifold optimization-based analysis dictionary learning with an ℓ1∕ 2-norm regularizer
Recently there has been increasing attention towards analysis dictionary learning. In
analysis dictionary learning, it is an open problem to obtain the strong sparsity-promoting …
analysis dictionary learning, it is an open problem to obtain the strong sparsity-promoting …
Diffusion-based Kalman iterative thresholding for compressed sampling recovery over network
Network-based CS recovery is used for faster processing of large-scale data, as well as for
sensor networks where the observation vector and sampling matrix are distributed. In this …
sensor networks where the observation vector and sampling matrix are distributed. In this …
Deterministic and randomized diffusion based iterative generalized hard thresholding (DiFIGHT) for distributed recovery of sparse signals
S Mukhopadhyay, M Chakraborty - IEEE Transactions on Signal …, 2021 - ieeexplore.ieee.org
In this paper, we propose a distributed iterative hard thresholding algorithm, namely,
DiFIGHT, for a network that uses diffusion as the means of intra-network collaboration …
DiFIGHT, for a network that uses diffusion as the means of intra-network collaboration …
Estimate exchange over network is good for distributed hard thresholding pursuit
We investigate an existing distributed algorithm for learning sparse signals or data over
networks. The algorithm is iterative and exchanges intermediate estimates of a sparse signal …
networks. The algorithm is iterative and exchanges intermediate estimates of a sparse signal …
Sparse deconvolution methods for online energy estimation in calorimeters operating in high luminosity conditions
T Teixeira, L Andrade, JM de Seixas - Journal of Instrumentation, 2021 - iopscience.iop.org
Energy reconstruction in calorimeters operating in high luminosity particle colliders has
become a remarkable challenge. In this scenario, pulses from a calorimeter front-end output …
become a remarkable challenge. In this scenario, pulses from a calorimeter front-end output …
Exploiting Node Level Algorithm Diversity for Distributed Compressed Sensing
KA Bapat, M Chakraborty - 2023 IEEE Asia Pacific Conference …, 2023 - ieeexplore.ieee.org
In this paper, we present heterogeneous hard thresholding (HHT) framework for distributed
compressed sensing (DCS). The proposed HHT framework takes into consideration the …
compressed sensing (DCS). The proposed HHT framework takes into consideration the …
Estimate exchange over network is good for distributed hard thresholding pursuit
We investigate an existing distributed algorithm for learning sparse signals or data over
networks. The algorithm is iterative and exchanges intermediate estimates of a sparse signal …
networks. The algorithm is iterative and exchanges intermediate estimates of a sparse signal …
Deterministic and randomized diffusion based iterative generalized hard thresholding (difight) for distributed sparse signal recovery
S Mukhopadhyay, M Chakraborty - arxiv preprint arxiv:1804.08265, 2018 - arxiv.org
In this paper we propose a distributed iterated hard thresholding algorithm termed DiFIGHT
over a network that is built on the diffusion mechanism and also propose a modification of …
over a network that is built on the diffusion mechanism and also propose a modification of …
Robust classification with sparse representation fusion on diverse data subsets
Sparse Representation (SR) techniques encode the test samples into a sparse linear
combination of all training samples and then classify the test samples into the class with the …
combination of all training samples and then classify the test samples into the class with the …
Compress sensing algorithm for estimation of signals in sensor networks
In this research, we present a data recovery scheme for wireless sensor networks. In some
sensor networks, each node must be able to recover the complete information of the …
sensor networks, each node must be able to recover the complete information of the …