This article introduces a novel fault classification method based on the mixture robust probabilistic linear discriminant analysis (MRPLDA). Unlike conventional probabilistic models like probabilistic principal compon...
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This article introduces a novel fault classification method based on the mixture robust probabilistic linear discriminant analysis (MRPLDA). Unlike conventional probabilistic models like probabilistic principal component analysis (PPCA), probabilistic linear discriminant analysis (PLDA) introduces two sets of latent variables to represent the within-class and between-class information, resulting in an enhanced classification capability. In order to deal with outliers and non-Gaussian distributed variables commonly encountered in industrial processes, a mixture of robust PLDA model is considered by imposing the Student's t-priors on the noise and hidden variables of the PLDA model. Based on the model, a variational bayesian expectation-maximization algorithm is developed for parameter estimation. In order to determine the state/class of a test sample, this article proposes a new state inference method by considering the joint probability between the test and training samples. The state inference method consists of a probability approximation, an evidence inference, and a voting based decision stage. The performance of the proposed fault classification method is illustrated by a numerical example and an application study to the Tennessee Eastman (TE) process. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.
In this paper, we introduce a training and compensation algorithm of the class-conditioned basis vectors in the non-negative matrix factorization (NMF) model for single-channel speech enhancement. The main goal is to ...
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In this paper, we introduce a training and compensation algorithm of the class-conditioned basis vectors in the non-negative matrix factorization (NMF) model for single-channel speech enhancement. The main goal is to estimate the basis vectors of different signal sources in a way that prevents them from representing each other, in order to reduce the residual noise components that have features similar to the speech signal. During the proposed training stage, the basis matrices for the clean speech and noises are estimated jointly by constraining them to belong to different classes. To this end, we employ the probabilistic generative model (PGM) of classification, specified by class-conditional densities, as an a priori distribution for the basis vectors. The update rules of the NMF and the PGM parameters of classification are jointly obtained by using the variational bayesian expectation-maximization (VBEM) algorithm, which guarantees convergence to a stationary point. Another goal of the proposed algorithm is to handle a mismatch between the characteristics of the training and test data. This is accomplished during the proposed enhancement stage, where we implement a basis compensation scheme. Specifically, we use extra free basis vectors to capture the features that are not included in the training data. Objective experimental results for different combination of speaker and noise types show that the proposed algorithm can provide better speech enhancement performance than the benchmark algorithms under various conditions. (C) 2018 Elsevier B.V. All rights reserved.
Background: To date, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants among a variety of traits/diseases, shedding light on the genetic architecture of complex ...
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Background: To date, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants among a variety of traits/diseases, shedding light on the genetic architecture of complex disease. The polygenicity of complex diseases is a widely accepted phenomenon through which a vast number of risk variants, each with a modest individual effect, collectively contribute to the heritability of complex diseases. This imposes a major challenge on fully characterizing the genetic bases of complex diseases. An immediate implication of polygenicity is that a much larger sample size is required to detect individual risk variants with weak/moderate effects. Meanwhile, accumulating evidence suggests that different complex diseases can share genetic risk variants, a phenomenon known as pleiotropy. Results: In this study, we propose a statistical framework for Leveraging Pleiotropic effects in large-scale GWAS data (LPG). LPG utilizes a variational bayesian expectation-maximization (VBEM) algorithm, making it computationally efficient and scalable for genome-wide-scale analysis. To demonstrate the advantages of LPG over existing methods that do not leverage pleiotropy, we conducted extensive simulation studies and applied LPG to analyze two pairs of disorders (Crohn's disease and Type 1 diabetes, as well as rheumatoid arthritis and Type 1 diabetes). The results indicate that by levelaging pleiotropy, LPG can improve the power of prioritization of risk variants and the accuracy of risk prediction. Conclusions: Our methodology provides a novel and efficient tool to detect pleiotropy among GWAS data for multiple traits/diseases collected from different studies. The software is available at https://***/Shufeyangyi2015310117/LPG.
In this paper, we introduce a supervised multi-channel speech enhancement algorithm based on a bayesian multi-channel non-negative matrix factorization (MNMF) model. In the proposed framework, we consider the probabil...
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ISBN:
(纸本)9781728112954
In this paper, we introduce a supervised multi-channel speech enhancement algorithm based on a bayesian multi-channel non-negative matrix factorization (MNMF) model. In the proposed framework, we consider the probabilistic generative model (PGM) of MNMF, specified by Poisson-distributed latent variables and gamma-distributed priors. In the training stage, the MNMF parameters of the speech and noise sources are estimated via the variational bayesian expectation-maximization (VBEM) algorithm. In the enhancement stage, the clean speech signal is estimated via the MNMF-based minimum variance distortionless response (MVDR) beamformer. To further improve the enhanced speech quality, we efficiently combine the MNMF-based beamforming technique with a classical unsupervised single-channel enhancement method. Experiments show that the proposed method can provide better enhancement performance than the selected benchmarks.
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