| 1. |
- Ma, Zhanyu, 1982-, et al.
(författare)
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A variational bayes beta mixture model for feature selection in DNA methylation studies
- 2013
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Ingår i: Journal of Bioinformatics and Computational Biology. - 0219-7200 .- 1757-6334. ; 11:4, s. 1350005-
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Tidskriftsartikel (refereegranskat)abstract
- An increasing number of studies are using beadarrays to measure DNA methylation on a genome-wide basis. The purpose is to identify novel biomarkers in a wide range of complex genetic diseases including cancer. A common difficulty encountered in these studies is distinguishing true biomarkers from false positives. While statistical methods aimed at improving the feature selection step have been developed for gene expression, relatively few methods have been adapted to DNA methylation data, which is naturally beta-distributed. Here we explore and propose an innovative application of a recently developed variational Bayesian beta-mixture model (VBBMM) to the feature selection problem in the context of DNA methylation data generated from a highly popular beadarray technology. We demonstrate that VBBMM offers significant improvements in inference and feature selection in this type of data compared to an Expectation-Maximization (EM) algorithm, at a significantly reduced computational cost. We further demonstrate the added value of VBBMM as a feature selection and prioritization step in the context of identifying prognostic markers in breast cancer. A variational Bayesian approach to feature selection of DNA methylation profiles should thus be of value to any study undergoing large-scale DNA methylation profiling in search of novel biomarkers.
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| 2. |
- Ma, Zhanyu, 1982-, et al.
(författare)
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Approximating the predictive distribution of the beta distribution with the local variational method
- 2011
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Ingår i: IEEE Intl. Workshop on Machine Learning for Signal Processing. - 9781457716232
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Konferensbidrag (refereegranskat)abstract
- In the Bayesian framework, the predictive distribution is obtained by averaging over the posterior parameter distribution. When there is a small amount of data, the uncertainty of the parameters is high. Thus with the predictive distribution, a more reliable result can be obtained in the applications as classification, recognition, etc. In the previous works, we have utilized the variational inference framework to approximate the posterior distribution of the parameters in the beta distribution by minimizing the Kullback-Leibler divergence of the true posterior distribution from the approximating one. However, the predictive distribution of the beta distribution was approximated by a plug-in approximation with the posterior mean, regardless of the parameter uncertainty. In this paper, we carry on the factorized approximation introduced in the previous work and approximate the beta function by its first order Taylor expansion. Then the upper bound of the predictive distribution is derived by exploiting the local variational method. By minimizing the upper bound of the predictive distribution and after normalization, we approximate the predictive distribution by a probability density function in a closed form. Experimental results shows the accuracy and efficiency of the proposed approximation method.
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| 3. |
- Ma, Zhanyu, 1982-, et al.
(författare)
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EEG Signal Classification with Super-Dirichlet Mixture Model
- 2012
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Ingår i: Processings of IEEE Statistical Signal Processing (SSP) Workshop 2012. - : IEEE. - 9781467301831 ; , s. 440-443
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Konferensbidrag (refereegranskat)abstract
- Classification of the Electroencephalogram (EEG) signal is a challengeable task in the brain-computer interface systems. The marginalized discrete wavelet transform (mDWT) coefficients extracted from the EEG signals have been frequently used in researches since they reveal features related to the transient nature of the signals. To improve the classification performance based on the mDWT coefficients, we propose a new classification method by utilizing the nonnegative and sum-to-one properties of the mDWT coefficients. To this end, the distribution of the mDWT coefficients is modeled by the Dirichlet distribution and the distribution of the mDWT coefficients from more than one channels is described by a super-Dirichletmixture model (SDMM). The Fisher ratio and the generalization error estimation are applied to select relevant channels, respectively. Compared to the state-of-the-art support vector machine (SVM) based classifier, the SDMM based classifier performs more stable and shows a promising improvement, with both channel selection strategies.
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| 4. |
- Ma, Zhanyu, 1982-, et al.
(författare)
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Vector Quantization of LSF Parameters With a Mixture of Dirichlet Distributions
- 2013
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Ingår i: IEEE Transactions on Audio, Speech, and Language Processing. - 1558-7916 .- 1558-7924. ; 21:9, s. 1777-1790
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Tidskriftsartikel (refereegranskat)abstract
- Quantization of the linear predictive coding parameters is an important part in speech coding. Probability density function (PDF)-optimized vector quantization (VQ) has been previously shown to be more efficient than VQ based only on training data. For data with bounded support, some well-defined bounded-support distributions (e.g., the Dirichlet distribution) have been proven to outperform the conventional Gaussian mixture model (GMM), with the same number of free parameters required to describe the model. When exploiting both the boundary and the order properties of the line spectral frequency (LSF) parameters, the distribution of LSF differences (Delta LSF) can be modelled with a Dirichlet mixture model (DMM). We propose a corresponding DMM based VQ. The elements in a Dirichlet vector variable are highly mutually correlated. Motivated by the Dirichlet vector variable's neutrality property, a practical non-linear transformation scheme for the Dirichlet vector variable can be obtained. Similar to the Karhunen-Loeve transform for Gaussian variables, this non-linear transformation decomposes the Dirichlet vector variable into a set of independent beta-distributed variables. Using high rate quantization theory and by the entropy constraint, the optimal inter-and intra-component bit allocation strategies are proposed. In the implementation of scalar quantizers, we use the constrained-resolution coding to approximate the derived constrained-entropy coding. A practical coding scheme for DVQ is designed for the purpose of reducing the quantization error accumulation. The theoretical and practical quantization performance of DVQ is evaluated. Compared to the state-of-the-art GMM-based VQ and recently proposed beta mixture model (BMM) based VQ, DVQ performs better, with even fewer free parameters and lower computational cost.
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| 5. |
- Rana, Pravin Kumar, 1982-, et al.
(författare)
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Multiview Depth Map Enhancement by Variational Bayes Inference Estimation of Dirichlet Mixture Models
- 2013
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Ingår i: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). - : IEEE. - 9781479903566 ; , s. 1528-1532
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Konferensbidrag (refereegranskat)abstract
- High quality view synthesis is a prerequisite for future free-viewpointtelevision. It will enable viewers to move freely in a dynamicreal world scene. Depth image based rendering algorithms willplay a pivotal role when synthesizing an arbitrary number of novelviews by using a subset of captured views and corresponding depthmaps only. Usually, each depth map is estimated individually bystereo-matching algorithms and, hence, shows lack of inter-viewconsistency. This inconsistency affects the quality of view synthesis negatively. This paper enhances the inter-view consistency ofmultiview depth imagery. First, our approach classifies the colorinformation in the multiview color imagery by modeling color witha mixture of Dirichlet distributions where the model parameters areestimated in a Bayesian framework with variational inference. Second, using the resulting color clusters, we classify the correspondingdepth values in the multiview depth imagery. Each clustered depthimage is subject to further sub-clustering. Finally, the resultingmean of each sub-cluster is used to enhance the depth imagery atmultiple viewpoints. Experiments show that our approach improvesthe average quality of virtual views by up to 0.8 dB when comparedto views synthesized by using conventionally estimated depth maps.
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