EM algorithm

Undirected graphical models or Markov random fields have been a popular class of models for representing conditional dependence relationships between nodes. In particular, Markov networks help us to understand complex interactions between genes in biological processes of a cell.

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With the advances in the next-generation sequencing technologies, researchers can now rapidly examine the composition of samples from humans and their surroundings. To enhance the accuracy of taxonomy assignments in metagenomic samples, we developed a method that allows multiple mismatch probabilities from different genomes.

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Primary ciliary dyskinesia (PCD) is a rare genetic disorder. Although the genetic tests and new diagnostic algorithms have recently been recommended, clinical signs and electron microscope (EM) findings have historically been the mainstays of diagnosis in Asia.

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Gene fusions, which result from abnormal chromosome rearrangements, are a pathogenic factor in cancer development. The emerging RNA-Seq technology enables us to detect gene fusions and profile their features.

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Binning environmental shotgun reads is one of the most fundamental tasks in metagenomic studies, in which mixed reads from different species or operational taxonomical units (OTUs) are separated into different groups. While dozens of binning methods are available, there is still room for improvement.

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Dominant markers in an F2 population or a hybrid population have much less linkage information in repulsion phase than in coupling phase. Linkage analysis produces two separate complementary marker linkage maps that have little use in disease association analysis and breeding.

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Clustering methods are becoming widely utilized in biomedical research where the volume and complexity of data is rapidly increasing. Unsupervised clustering of patient information can reveal distinct phenotype groups with different underlying mechanism, risk prognosis and treatment response.

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Unsupervised clustering represents one of the most widely applied methods in analysis of highthroughput ‘omics data. A variety of unsupervised model-based or parametric clustering methods and nonparametric clustering methods have been proposed for RNA-seq count data, most of which perform well for large samples, e.g. N ≥ 500.

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We previously introduced a random-effects model to analyze a set of patients, each of which has two distinct tumors. The goal is to estimate the proportion of patients for which one of the tumors is a metastasis of the other, i.e. where the tumors are clonally related. Matches of mutations within a tumor pair provide the evidence for clonal relatedness.

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In complex indoor environments, due to the attenuation of the signal and the changing surrounding environment, the censoring and multi-component problems may be present in the observed data. Censoring refers to the fact that sensors on portable devices cannot measure Received Signal Strength Index (RSSI) values below a specific threshold, such as -100 dBm. The multi-component problem occurs when the measured data varies due to obstacles and user directions, whether the door is closed or open, etc.

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In the Wireless Local Area Network (WLAN), due to the unexpected operation of equipments and the changing of surround environment, the dropping and multi-component problems might present in the observed data. Dropping refers to the fact that occasionally Received Signal Strength Indication (RSSI) measurements of Wi-Fi access points (AP) are not available, although their value is clearly above the limited sensitivity of Wi-Fi sensors on portable devices.

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For clustering problems based on the model of mixture probability distribution separation, we propose new Variable Neighbourhood Search algorithms (VNS) and evolutionary genetic algorithms (GA) with greedy agglomerative heuristic procedures and compare them with known algorithms. New genetic algorithms implement a global search strategy with the use of a special crossover operator based on greedy agglomerative heuristic procedures in combination with the EM algorithm (Expectation Maximization).

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In this paper, we mainly consider the discrimination between these distributions. It is observed that the maximum likelihood estimators (MLEs) cannot be obtained in closed form.

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In this paper electromagnetism (EM) metaheuristic is used for solving the NPhard strong minimum energy topology problem (SMETP). Objective function is adapted to the problem so that it effectively prevents infeasible solutions. Proposed EM algorithm uses efficient local search to speed up overall running time. This approach is tested on two sets of randomly generated symmetric and asymmetric instances.

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Chapter 6: Unsupervised Learning – Clustering Introduction to unsupervised learning and clustering, Partitional clustering (k-Means algorithm), Hierarchical clustering, Expectation Maximization (EM) algorithm, Incremental Clustering.

48p cocacola_10 08-12-2015 38 1   Download

We propose a new method of classifying documents into categories. We define for each category a finite mixture model based on soft clustering of words. We treat the problem of classifying documents as that of conducting statistical hypothesis testing over finite mixture models, and employ the EM algorithm to efficiently estimate parameters in a finite mixture model. Experimental results indicate that our method outperforms existing methods.

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We propose an unsupervised, iterative method for detecting downward-entailing operators (DEOs), which are important for deducing entailment relations between sentences. Like the distillation algorithm of Danescu-Niculescu-Mizil et al. (2009), the initialization of our method depends on the correlation between DEOs and negative polarity items (NPIs). However, our method trusts the initialization more and aggressively separates likely DEOs from spurious distractors and other words, unlike distillation, which we show to be equivalent to one iteration of EM prior re-estimation.

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In this paper, we first demonstrate the interest of the Loopy Belief Propagation algorithm to train and use a simple alignment model where the expected marginal values needed for an efficient EM-training are not easily computable. We then improve this model with a distortion model based on structure conservation.

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We present an algorithm for pronounanaphora (in English) that uses Expectation Maximization (EM) to learn virtually all of its parameters in an unsupervised fashion. While EM frequently fails to find good models for the tasks to which it is set, in this case it works quite well. We have compared it to several systems available on the web (all we have found so far). Our program significantly outperforms all of them. The algorithm is fast and robust, and has been made publically available for downloading....

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Probabilistic Latent Semantic Analysis (PLSA) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis (LSA). However, the parameters of a PLSA model are trained using the Expectation Maximization (EM) algorithm, and as a result, the trained model is dependent on the initialization values so that performance can be highly variable. In this paper we present a method for using LSA analysis to initialize a PLSA model.

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