Fast and incoherent dictionary learning algorithms with application to fMRI

Abolghasemi, Vahid; Ferdowsi, Saideh; Sanei, Saeid

doi:10.1007/s11760-013-0429-2

Cited by 78 publications

(51 citation statements)

References 34 publications

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“…p n l (t) = e j2π f n l t 0≤t≤T p , 0 otherwise, (2) where N is the total number of pulses, T p the sub-pulse width, L is the number of sub-pulses in n-th signal; the l-th sub-pulse frequency of the n-th signal is f n l (t) = f n 0 + (l − 1)∆ f , ∆ f = 1/T p , the frequency coding sequence of the n-th signal is expressed as…”

Section: Model Of Mimo Radar Signalsmentioning

confidence: 99%

“…It can extract the source signals from the observed signals under the condition that little priori knowledge of sources or channel is obtained except for the independence of signals. In recent years, as a popular signal processing method, it has been successfully applied to many fields, such as: voice signal processing [1], biomedical engineering [2], array signal processing [3], image processing [4], mechanical fault diagnosis [5] and so on. According to the different numbers of observed signals and sources, blind source separation can be divided into three cases including undetermined, normal and overdetermined.…”

Section: Introductionmentioning

confidence: 99%

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Mixing Matrix Estimation of Underdetermined Blind Source Separation Based on Data Field and Improved FCM Clustering

Guo

Ruan

2018

Symmetry

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Abstract:In modern electronic warfare, multiple input multiple output (MIMO) radar has become an important tool for electronic reconnaissance and intelligence transmission because of its anti-stealth, high resolution, low intercept and anti-destruction characteristics. As a common MIMO radar signal, discrete frequency coding waveform (DFCW) has a serious overlap of both time and frequency, so it cannot be directly used in the current radar signal separation problems. The existing fuzzy clustering algorithms have problems in initial value selection, low convergence rate and local extreme values which will lead to the low accuracy of the mixing matrix estimation. Consequently, a novel mixing matrix estimation algorithm based on data field and improved fuzzy C-means (FCM) clustering is proposed. First of all, the sparsity and linear clustering characteristics of the time-frequency domain MIMO radar signals are enhanced by using the single-source principal value of complex angular detection. Secondly, the data field uses the potential energy information to analyze the particle distribution, thus design a new clustering number selection scheme. Then the particle swarm optimization algorithm is introduced to improve the iterative clustering process of FCM, and finally get the estimated value of the mixing matrix. The simulation results show that the proposed algorithm improves both the estimation accuracy and the robustness of the mixing matrix.

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Section: Model Of Mimo Radar Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mixing Matrix Estimation of Underdetermined Blind Source Separation Based on Data Field and Improved FCM Clustering

Guo

Ruan

2018

Symmetry

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“…The threshold used to replace similar rows in ASimCO-Random is also set as µ 0 to be consistent with the coherence limit of Incoherent Analysis SimCO. The parameters for the decorrelation method in ASimCO-IKSVD are set as recommended in [29].…”

Section: ) Simulations With Different Parametersmentioning

confidence: 99%

“…Compared with this method, the decorrelation step applied in Incoherent Analysis SimCO can better preserve the information in the dictionary atoms since the new atoms are generated by rotating the existing atoms to be replaced. The method in Incoherent K-SVD (IK-SVD) [29], proposed for the synthesis model, can also be used to decorrelate the atoms of the analysis dictionary, which can be achieved by minimizing ΩΩ T − I 2 F after the update of the dictionary. However, this method cannot directly control the degree of the coherence of the dictionary.…”

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confidence: 99%

Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning

Dong

Wang

Dai

et al. 2016

IEEE Trans. Signal Process.

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Abstract-In this paper, we consider the dictionary learning problem for the sparse analysis model. A novel algorithm is proposed by adapting the simultaneous codeword optimization (SimCO) algorithm, based on the sparse synthesis model, to the sparse analysis model. This algorithm assumes that the analysis dictionary contains unit ℓ2-norm atoms and learns the dictionary by optimization on manifolds. This framework allows multiple dictionary atoms to be updated simultaneously in each iteration. However, similar to several existing analysis dictionary learning algorithms, dictionaries learned by the proposed algorithm may contain similar atoms, leading to a degenerate (coherent) dictionary. To address this problem, we also consider restricting the coherence of the learned dictionary and propose Incoherent Analysis SimCO by introducing an atom decorrelation step following the update of the dictionary. We demonstrate the competitive performance of the proposed algorithms using experiments with synthetic data and image denoising as compared with existing algorithms.

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“…These studies include, among others, sparse representation of nonparametric components of partially linear models [12] and the sparseness of the activity related BOLD signal by using "activelets" [13]. Additionally, methods based on sparse dictionary learning for detecting activated regions in fMRI data have been proposed, including data-driven sparse GLM [14], fast incoherent dictionary learning (FIDL) [15], and sparse representation of whole-brain fMRI signals using an online dictionary learning approach [16]. In parallel, sparse models have been proposed to predict or decode task parameters from individual fMRI activity patterns by using methods such as the elastic net method [17], generalized sparse classifiers [18], or multivariate pattern analysis based on sparse representation models [19,20].…”

Section: Introductionmentioning

confidence: 99%

Detecting Activation in fMRI Data: An Approach Based on Sparse Representation of BOLD Signal

Guillen

Paredes²,

Medina

2018

Mathematical Problems in Engineering

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This paper proposes a simple yet effective approach for detecting activated voxels in fMRI data by exploiting the inherent sparsity property of the BOLD signal in temporal and spatial domains. In the time domain, the approach combines the General Linear Model (GLM) with a Least Absolute Deviation (LAD) based regression method regularized by the pseudonorm ℓ 0 to promote sparsity in the parameter vector of the model. In the spatial domain, detection of activated regions is based on thresholding the spatial map of estimated parameters associated with a particular stimulus. The threshold is calculated by exploiting the sparseness of the BOLD signal in the spatial domain assuming a Laplacian distribution model. The proposed approach is validated using synthetic and real fMRI data. For synthetic data, results show that the proposed approach is able to detect most activated voxels without any false activation. For real data, the method is evaluated through comparison with the SPM software. Results indicate that this approach can effectively find activated regions that are similar to those found by SPM, but using a much simpler approach. This study may lead to the development of robust spatial approaches to further simplifying the complexity of classical schemes.

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Fast and incoherent dictionary learning algorithms with application to fMRI

Cited by 78 publications

References 34 publications

Mixing Matrix Estimation of Underdetermined Blind Source Separation Based on Data Field and Improved FCM Clustering

Mixing Matrix Estimation of Underdetermined Blind Source Separation Based on Data Field and Improved FCM Clustering

Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning

Detecting Activation in fMRI Data: An Approach Based on Sparse Representation of BOLD Signal

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