Improved M-FOCUSS Algorithm With Overlapping Blocks for Locally Smooth Sparse Signals

Zdunek, Rafał; Cichocki, Andrzej

doi:10.1109/tsp.2008.928160

Cited by 55 publications

(39 citation statements)

References 85 publications

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“…However, smoothness is a special kind of correlation structures. It is not helpful when adjacent regression coefficients are not smooth (but still correlated), as shown in [20] in the comparison between the T-MSBL algorithm [20] and a smoothness constrained sparse algorithm in [51]. More general correlation structures were studied in [10], [20], [21], which did not constrain the smoothness in regression coefficients.…”

Section: Discussionmentioning

confidence: 99%

Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning

Wan

Zhang

Rao

et al. 2014

IEEE Trans. Med. Imaging

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Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer’s disease (AD). Traditionally, this task is performed by formulating a linear regression problem. Recently, it is found that using a linear sparse regression model can achieve better prediction accuracy. However, most existing studies only focus on the exploitation of sparsity of regression coefficients, ignoring useful structure information in regression coefficients. Also, these linear sparse models may not capture more complicated and possibly nonlinear relationships between cognitive performance and MRI measures. Motivated by these observations, in this work we build a sparse multivariate regression model for this task and propose an empirical sparse Bayesian learning algorithm. Different from existing sparse algorithms, the proposed algorithm models the response as a nonlinear function of the predictors by extending the predictor matrix with block structures. Further, it exploits not only inter-vector correlation among regression coefficient vectors, but also intra-block correlation in each regression coefficient vector. Experiments on the Alzheimer’s Disease Neuroimaging Initiative database showed that the proposed algorithm not only achieved better prediction performance than state-of-the-art competitive methods, but also effectively identified biologically meaningful patterns.

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Section: Discussionmentioning

confidence: 99%

Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning

Wan

Zhang

Rao

et al. 2014

IEEE Trans. Med. Imaging

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“…The FOCUSS algorithm is based on the iteratively reweighted least squares algorithm, and enforces a certain degree of sparsity [28]. The sparsity is determined by λ (the sparsity of u increases with λ), and λ balances between sparsity and the residual error.…”

Section: Methodsmentioning

confidence: 99%

“…The GCV-FOCUSS+ algorithm is based on FOCUSS+ [20] that solves for nonnegative u such that u has a certain maximum sparsity n (i.e., n = 22 for the HPA axis), and uses the generalized cross-validation (GCV) technique [9] for estimating the regularization parameter. In particular, GCV-FOCUSS+ is closely related to a version of the FOCUSS algorithm by Zdunek et al [28], which uses the GCV technique for updating the regularization parameter λ. Choosing a λ value that balances between the noise and sparsity is important in detecting the sparsity level.…”

Section: Methodsmentioning

confidence: 99%

Quantifying Pituitary-Adrenal Dynamics and Deconvolution of Concurrent Cortisol and Adrenocorticotropic Hormone Data by Compressed Sensing

Faghih

Dahleh

Adler

et al. 2015

IEEE Trans. Biomed. Eng.

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Pulsatile release of cortisol from the adrenal glands is governed by pulsatile release of adrenocorticotropic hormone (ACTH) from the anterior pituitary. In return, cortisol has a negative feedback effect on ACTH release. Simultaneous recording of ACTH and cortisol is not typical, and determining the number, timing, and amplitudes of pulsatile events from simultaneously recorded data is challenging because of several factors: (I) stimulator ACTH pulse activity, (II) kinematics of ACTH and cortisol, (III) the sampling interval, and (IV) the measurement error. We model ACTH and cortisol secretion simultaneously using a linear differential equations model with Gaussian errors and sparse pulsatile events as inputs to the model. We propose a novel framework for recovering pulses and parameters underlying the interactions between ACTH and cortisol. We recover the timing and amplitudes of pulses using compressed sensing, and employ generalized cross validation for determining the number of pulses. We analyze serum ACTH and cortisol levels sampled at 10-minute intervals over 24 hours from 10 healthy women. We recover physiologically plausible timing and amplitudes for these pulses and model the feedback effect of cortisol. We recover 15 to 18 pulses over 24 hours, which is highly consistent with the results of another cortisol data analysis approach. Modeling the interactions between ACTH and cortisol allows for accurate quantification of pulsatile events, and normal and pathological states. This could lay the basis for a more physiologically-based approach for administering cortisol therapeutically. The proposed approach can be adapted to deconvolve other pairs of hormones with similar interactions.

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“…Based on the "sparsity" assumption, the compressed sensing techniques are suitable for such situations by estimating the angular spectrum from underdetermined system of equations. In particular, the MFOCUSS [5] algorithm is used. The simulation example in Figure 9 again assumes the array antenna pattern based on the single subarray in Figure 1.…”

Section: Compressive Sensing Solutionmentioning

confidence: 99%

Super-resolution technologies for all-weather sense and avoidance (SAA) radar

Zhang

Wang

et al. 2011

SPIE Proceedings

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The sense and avoidance (SAA) and due-regard radar systems have strict requirements on size, weight and power (SWaP) and target localization accuracies. Also, the multi-mission capabilities with both weather and hard targets are critical to the survivability of unmanned aerial vehicles (UAV) in the next generation national airspace. The aperture limitations of the aircraft sensor installation, however, have prevented large antennas/arrays to be used. The tradeoffs among frequencies, resolutions and detection range/accuracies have not been fully addressed. Innovative concepts of overcoming the aperture limitation by using a special type of super-resolution technology are introduced. The first technique is based on a combination of thinned antenna array, an extension to the traditional Multiple Signal Classification (MUSIC) technique, and applying a two-dimensional sidelobe mitigation technique. To overcome the degradation of MUSIC-type of approach due to coherent radar signals, a special waveform optimization procedure is used. The techniques for mitigating artifacts due to "thinned" array are also introduced. Simulated results of superresolution techniques are discussed and evaluated, and the capability of separating multiple targets within apertureconstrained beamwidth is demonstrated. Moreover, the potential capabilities of autonomous weather hazard avoidance are also analyzed.

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Improved M-FOCUSS Algorithm With Overlapping Blocks for Locally Smooth Sparse Signals

Cited by 55 publications

References 85 publications

Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning

Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning

Quantifying Pituitary-Adrenal Dynamics and Deconvolution of Concurrent Cortisol and Adrenocorticotropic Hormone Data by Compressed Sensing

Super-resolution technologies for all-weather sense and avoidance (SAA) radar

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