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Showing 1 to 20 of 52 for “"Expectation-maximization (EM) algorithm"”.
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Using the EM Algorithm to Estimate the Difference in Dependent Proportions in a 2 x 2 Table with Missing Data.
… table when there are missing data. The Expectation-Maximization (EM) algorithm is used to obtain an estimate for the difference between correlated proportions. To obtain the standard error of this difference I employ a resampling technique known as bootstrapping. The performance of the …
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Application of the EM Algorithm for Mixture Models
… that identifies trajectories by using the Expectation-Maximization (EM) algorithm to fit semi-parametric mixtures of logistic distributions to longitudinal binary data. For performance comparison, we consider full maximization algo rithms (e.g. SAS procedure PROC TRAJ) and standard EM, as …
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Unsupervised modeling of latent topics and lexical units in speech audio
… discovers important words, phrases and topical themes present in an audio corpus. This system employs a segmental dynamic time warping (S-DTW) algorithm for acoustic pattern discovery in conjunction with a probabilistic model which treats the topic and pseudo-word identity of each discovered …
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Accelerated clustering through locality-sensitive hashing
We obtain improved running times for two algorithms for clustering data: the expectation-maximization (EM) algorithm and Lloyd's algorithm. The EM algorithm is a heuristic for finding a mixture of k normal distributions in Rd that maximizes the probability of drawing n given data points. Lloyd's …
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Planar detection using modified expectation maximization
… an image pair is cast as an incomplete data problem where the parameters to be estimated are the ones that define the homographies induced by the planar regions in the scene. This incomplete data problem motivates the employment of the Expectation Maximization (EM) algorithm. Derivation of the EM …
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An EM algorithm for Lidar deconvolution
… return from the observed return signal. The expectation-maximization (EM) algorithm has been used in signal deconvolution, to produce a maximum-likelihood estimate (MLE) for the original signal. We explain the benefits of the EM algorithm over other benchmark algorithms in Lidar …
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Discriminative, generative, and imitative learning
… machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and …
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Knowledge discovery and modeling in genomic databases
… acceptor sites in vertebrate genes. The HMM system based on the developed models is fully trained using an expectation maximization (EM) algorithm and the system performance is evaluated using a 10-way cross-validation method. Experimental results show that the proposed HMM system achieves high …
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A Study of Reject Inference Techniques
… average. To estimate these parameters, we usethe Expectation-Maximization (EM) algorithm wherein the data associated with therejected applicants is treated as missing completely at random. Simulated data derivedfrom actual case studies are used to assess the effectiveness of the mixture …
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A Minimax Approach for Learning Gaussian Mixtures
… a non-convex concave minimax optimization problem. We show that a Gradient Descent Ascent (GDA) method converges to an approximate stationary minimax point of the GAT-GMM optimization problem. In the benchmark case of a mixture of two symmetric, well-separated Gaussians, we further show this …
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Domain Adaptation with a Classifier Trained by Robust Pseudo-Labels
… power, approaches based on deep learning algorithms have achieved remarkable results in solving computer vision classification problems. These performance improvements are achieved by assuming the source and target data are collected from the same probability distribution. However, this …
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Instrument Timbres and Pitch Estimation in Polyphonic Music
… pieces of music by melody; automatic annotation algorithms seek to enable finer search criteria, such as instruments, genre, or meter. Score transcription systems strive for an abstract, compressed form of a piece of music understandable by composers and musicians. Much research still has to be …
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Event detection in EEG signals for brain computer interface using expectation-maximation algorithm
… electroencephalography (EEG)) and translating them into machine-understandable language. Most of the current BCI systems identify features of the brain signals and classify them according to a predefined criterion set by the classifying algorithm. The features of the brain signals can change over …
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Blind separation of noisy multivariate data using second-order statistics
… non-stationarity of the underlying sources. The algorithm estimates the Second-Order separation transform A, the signal Order, and Noise, and is therefore referred to as SOON. SOON iteratively estimates: 1) k using a scree metric, and 2) the values of AP, G, and W using the …
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Algorithms And Tools For Computational Analysis Of Human Transcriptome Using Rna-Seq
… <p> We have developed two novel algorithms and tools and a computational workflow to interrogate human transcriptomes between healthy and diseased conditions. The first is a read count-based Expectation-Maximization (EM) algorithm and tool, which is called RAEM. It estimates …
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High-Dimensional Functional Graphs and Inference for Unknown Heterogeneous Populations
… conditional dependence structure among random elements. We mainly focus on the following three research projects. The first project combines the strengths of FGMs with finite mixture of regression models (FMR) to overcome the challenges of estimating conditional dependence structures from …
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Corporate Default Predictions and Methods for Uncertainty Quantifications
… estimating parameters in the DFM, we derive an expectation maximization (EM) algorithm in explicit forms under necessary constraints. For multi-period default risks, we consider both the corporate-level and the market-level predictions. We also develop prediction interval (PI) procedures that …
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Some new Laplace-based probability distributions for modeling data with pronounced peaks combined with heavy tails and outliers
… estimation through various computational schemes. This includes leveraging the Expectation Maximization (EM) algorithm and its variants, and addressing computational issues related to the models. We investigate the effectiveness of these estimation strategies on simulated data and demonstrate …
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State-space models for stream networks
… of this exact likelihood, a version of the expectation-maximization (EM) algorithm is presented that uses the Kalman Smoother to fill in missing values in the E-step, and maximizes the Gaussian likelihood for the completed dataset in the M-step. Several forms of dependence for discrete …
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Localized Feature Selection For Unsupervised Learning
… In general, unsupervised feature selection algorithms conduct feature selection in a global sense by producing a common feature subset for all the clusters. This, however, can be invalid in clustering practice, where the local intrinsic property of data matters more, which implies that …
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