Orthogonal Least Absolute Value for Sparse Spike Deconvolution
Sign Up to like & getrecommendations! Published in 2021 at "Circuits, Systems, and Signal Processing"
DOI: 10.1007/s00034-021-01667-z
Abstract: Several phenomena encountered in nature are characterized by very localized events occurring randomly at given times. Random pulses are an appropriate modelling tool for such events. Usually, the impulses are hidden in the noise due… read more here.
Keywords: orthogonal least; least absolute; deconvolution; absolute value ... See more keywords
One-layer neural network for solving least absolute deviation problem with box and equality constraints
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.11.037
Abstract: Abstract This paper presents a neural network for solving least absolute deviation problems with equality and box constraints. Compared with some existing models, the proposed neural network has fewer state variables and only one-layer structure.… read more here.
Keywords: absolute deviation; network; solving least; network solving ... See more keywords
Robust kernelised correlation filter tracking with least absolute deviations regression
Sign Up to like & getrecommendations! Published in 2018 at "Electronics Letters"
DOI: 10.1049/el.2017.2147
Abstract: Kernelised correlation filter (KCF) has demonstrated its superior performance in visual tracking. The strength of the approach stems from its ability to efficiently learn the object appearance variations over time. A fundamental drawback to KCF,… read more here.
Keywords: regression; deviations regression; correlation filter; least absolute ... See more keywords
Regularized least absolute deviation-based sparse identification of dynamical systems.
Sign Up to like & getrecommendations! Published in 2023 at "Chaos"
DOI: 10.1063/5.0130526
Abstract: This work develops a regularized least absolute deviation-based sparse identification of dynamics (RLAD-SID) method to address outlier problems in the classical metric-based loss function and the sparsity constraint framework. Our method uses absolute derivation loss… read more here.
Keywords: least absolute; identification; absolute deviation; based sparse ... See more keywords
Sparse Autoregressive Modeling via the Least Absolute LP-Norm Penalized Solution
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2908189
Abstract: The conventional autoregressive (AR) model has been widely applied in the various electroencephalogram (EEG) analyses such as spectrum estimation, waveform fittings, and in classification tasks. Nevertheless, evoked EEG is usually inevitably contaminated by multiple background… read more here.
Keywords: modeling via; sparse autoregressive; penalized solution; autoregressive modeling ... See more keywords
A Joint Least Squares and Least Absolute Deviation Model
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2019.2897863
Abstract: We propose a joint least squares and least absolute deviations (JOLESALAD) model, show that the proposed model can cover least absolute shrinkage and selection operator (LASSO) and two of its variants, namely the generalized LASSO (gLASSO) and… read more here.
Keywords: squares least; least squares; joint least; model ... See more keywords