Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization

Boukouvalas, Zois; Levin-Schwartz, Yuri; Mowakeaa, Rami; Fu, Geng

doi:10.1109/ssp.2018.8450858

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…However, the probability density distribution of the actual signal does not necessarily satisfy this assumption. Only when the probability density of the signal is close to the estimated probability density can the algorithm achieve better separation performance [ 38 ]. Considering that the signal characteristics of the source signal in the simulated signal are known, the proposed algorithm adopts the following flow to process the simulated signal.…”

Section: Simulation Analysis Of Algorithm Performancementioning

confidence: 99%

“…Next, we discuss the influence of the probability distribution type of the estimated source signal on the performance of the algorithm from two aspects: The algorithm employs different PDFs corresponding to those in Table 2 for the simulated signals of the sub-Gaussian distribution, aiming to verify the performance of the proposed algorithm under the model mismatch condition. The algorithm for selecting the sub-Gaussian distribution is compared with ICA-EBM [ 9 ] and ICA-EMK [ 38 ] to verify the effectiveness of the proposed algorithm. …”

Section: Simulation Analysis Of Algorithm Performancementioning

confidence: 99%

“…The algorithm for selecting the sub-Gaussian distribution is compared with ICA-EBM [ 9 ] and ICA-EMK [ 38 ] to verify the effectiveness of the proposed algorithm.…”

Section: Simulation Analysis Of Algorithm Performancementioning

confidence: 99%

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Blind Source Separation Method Based on Neural Network with Bias Term and Maximum Likelihood Estimation Criterion

Liu

Wang

Zhang

2021

Sensors

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Convergence speed and steady-state source separation performance are crucial for enable engineering applications of blind source separation methods. The modification of the loss function of the blind source separation algorithm and optimization of the algorithm to improve its performance from the perspective of neural networks (NNs) is a novel concept. In this paper, a blind source separation method, combining the maximum likelihood estimation criterion and an NN with a bias term, is proposed. The method adds L2 regularization terms for weights and biases to the loss function to improve the steady-state performance and designs a novel optimization algorithm with a dual acceleration strategy to improve the convergence speed of the algorithm. The dual acceleration strategy of the proposed optimization algorithm smooths and speeds up the originally steep, slow gradient descent in the parameter space. Compared with competing algorithms, this strategy improves the convergence speed of the algorithm by four times and the steady-state performance index by 96%. In addition, to verify the source separation performance of the algorithm more comprehensively, the simulation data with prior knowledge and the measured data without prior knowledge are used to verify the separation performance. Both simulation results and validation results based on measured data indicate that the new algorithm not only has better convergence and steady-state performance than conventional algorithms, but it is also more suitable for engineering applications.

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Section: Simulation Analysis Of Algorithm Performancementioning

confidence: 99%

Section: Simulation Analysis Of Algorithm Performancementioning

confidence: 99%

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Blind Source Separation Method Based on Neural Network with Bias Term and Maximum Likelihood Estimation Criterion

Liu

Wang

Zhang

2021

Sensors

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“…From (13), we can achieve perfect source recovery as long as the assumed model PDF matches the true latent multivariate density of the nth SCV, i.e., f (p(yn),p(yn)) = 0. Motivated by the flexibility and superior performance of ICA-EMK [27] in the univariate case, we use M-EMK to derive a novel IVA algorithm, IVA-M-EMK, which takes advantage of the accurate estimation capability of M-EMK to greatly improve separation performance. To achieve this, we use a decoupling procedure [27][28][29], to minimize (2) with respect to each row vector w…”

Section: Iva-m-emkmentioning

confidence: 99%

Independent Vector Analysis Using Semi-Parametric Density Estimation via Multivariate Entropy Maximization

Damasceno

Cavalcante

Boukouvalas

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Due to the wide use of multi-sensor technology, analysis of multiple sets of data is at the heart of many challenging engineering problems. Independent vector analysis (IVA), a recent generalization of independent component analysis (ICA), enables the joint analysis of datasets and extraction of latent sources through the use of a simple yet effective generative model. However, the success of IVA is tied to proper estimation of the probability density function (PDF) of the multivariate latent sources; information that is generally unknown. In this work, we propose a new flexible and efficient multivariate PDF estimation technique based on the maximum entropy principle and apply this technique to the development of an effective IVA algorithm that successfully matches multivariate latent sources from a wide range of distributions. We verify the effectiveness of the new estimation technique and further demonstrate the superior performance of the new IVA algorithm numerically using simulated data.

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The Case for Latent Variable Vs Deep Learning Methods in Misinformation Detection: An Application to COVID-19

et al. 2021

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Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization

Cited by 6 publications

References 9 publications

Blind Source Separation Method Based on Neural Network with Bias Term and Maximum Likelihood Estimation Criterion

Blind Source Separation Method Based on Neural Network with Bias Term and Maximum Likelihood Estimation Criterion

Independent Vector Analysis Using Semi-Parametric Density Estimation via Multivariate Entropy Maximization

The Case for Latent Variable Vs Deep Learning Methods in Misinformation Detection: An Application to COVID-19

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