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1. Sparse Autoregressive Modeling via the Least Absolute LP-Norm Penalized Solution

Author

Joyce Chelangat Bore, Walid Mohammed Ahmed Ayedh, Peiyang Li, Dezhong Yao, and Peng Xu

Subjects
Autoregressive model, outliers, sparse solution, visual oddball, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
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 activities (ongoing EEG) as well as the strong outliers which may distort the AR estimates of various AR estimation methods including LS, Yule-Walker, and Burg. Moreover, current AR approaches perform well only when the length of the time-series is much larger than the number of brain sites studied, which is exactly the reverse of the situation in neuroimaging whereby relatively short time-series are measured over thousands of voxels thus the need for penalized methods to obtain sparse solutions. In this paper, we introduce a novel ADMM-based AR estimator termed LAPPS (Least Absolute LP (0

Published
2019
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2. Sparse Autoregressive Modeling via the Least Absolute LP-Norm Penalized Solution

Author

Peng Xu, Peiyang Li, Dezhong Yao, Joyce Chelangat Bore, and Walid Mohammed Ahmed Ayedh

Subjects
General Computer Science, Computer science, 02 engineering and technology, visual oddball, Residual, Autoregressive model, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Spurious relationship, business.industry, outliers, General Engineering, Estimator, Spectral density, Pattern recognition, Function (mathematics), sparse solution, Noise, Outlier, 020201 artificial intelligence & image processing, Artificial intelligence, lcsh:Electrical engineering. Electronics. Nuclear engineering, business, lcsh:TK1-9971, 030217 neurology & neurosurgery
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 activities (ongoing EEG) as well as the strong outliers which may distort the AR estimates of various AR estimation methods including LS, Yule-Walker, and Burg. Moreover, current AR approaches perform well only when the length of the time-series is much larger than the number of brain sites studied, which is exactly the reverse of the situation in neuroimaging whereby relatively short time-series are measured over thousands of voxels thus the need for penalized methods to obtain sparse solutions. In this paper, we introduce a novel ADMM-based AR estimator termed LAPPS (Least Absolute LP (0

Published
2019

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