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IEEE International Conference on Big Data

作者： AlJadda, Khalifeh Korayem, Mohammed Ortiz, Camilo Grainger, Trey Miller, John A. York, William S. Univ Georgia Dept Comp Sci Athens GA 30602 USA Indiana Univ Sch Informat & Comp Bloomington IN USA CareerBuilder Norcross GA USA Univ Georgia Complex Carbohydrate Res Ctr Athens GA 30602 USA

ISBN: (纸本)9781479956661

In the big data era, scalability has become a crucial requirement for any useful computational model. probabilistic graphical models are very useful for mining and discovering data insights, but they are not scalable enough to be suitable for big data problems. Bayesian Networks particularly demonstrate this limitation when their data is represented using few random variables with a massive set of outcome values for each of them. With hierarchical data -data that is arranged in a treelike structure with several levels -one would expect to see hundreds of thousands or millions of values distributed over even just a small number of levels. When modeling this kind of hierarchical data across large data sets, Bayesian networks become unsuitable for representing the probability distributions for the following reasons: i) each level represents a single random variable with hundreds of thousands of values, ii) the number of levels is usually small, so there are also few random variables, and iii) the structure of the network is predefined since the dependency is modeled top-down from each parent to each of its child nodes. In this paper we propose a scalable probabilistic graphical model to overcome these limitations for massive hierarchical data. We believe the proposed model will lead to an easily-scalable, more readable, and expressive implementation for problems that require probabilistic-based solutions for massive amounts of hierarchical data. We successfully applied this model to solve two different challenging probabilistic-based problems on massive hierarchical data sets for different domains, namely, bioinformatics and latent semantic discovery over search logs.

关键词： Bayes methods Big Data data mining trees (mathematics) Bayesian networks PGMHD bioinformatics data insight discovery data insight mining latent semantic discovery massive hierarchical data problems probabilistic graphical models probability distributions scalable probabilistic graphical model treelike structure Data models graphical models probabilistic logic Probability distribution Random variables Semantics

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Word-level language modeling for P300 spellers based on discriminative graphical models

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JOURNAL OF NEURAL ENGINEERING 2015年第2期12卷 026007-026007页

作者： Saa, Jaime F. Delgado de Pesters, Adriana McFarland, Dennis Cetin, Mujdat Sabanci Univ Signal Proc Info Syst Lab Istanbul Turkey Univ Norte Robot & Intelligent Syst Lab Barranquilla Colombia Wadsworth Ctr Lab Injury & Repair Albany NY USA

Objective. In this work we propose a probabilistic graphical model framework that uses language priors at the level of words as a mechanism to increase the performance of P300-based spellers. Approach. This paper is concerned with brain-computer interfaces based on P300 spellers. Motivated by P300 spelling scenarios involving communication based on a limited vocabulary, we propose a probabilistic graphical model framework and an associated classification algorithm that uses learned statistical models of language at the level of words. Exploiting such high-level contextual information helps reduce the error rate of the speller. Main results. Our experimental results demonstrate that the proposed approach offers several advantages over existing methods. Most importantly, it increases the classification accuracy while reducing the number of times the letters need to be flashed, increasing the communication rate of the system. Significance. The proposed approach models all the variables in the P300 speller in a unified framework and has the capability to correct errors in previous letters in a word, given the data for the current one. The structure of the model we propose allows the use of efficient inference algorithms, which in turn makes it possible to use this approach in real-time applications.

关键词： brain computer interfaces probabilistic graphical models language models P300 speller inference algorithms

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Using probabilistic models for Missing Data Prediction in Network Industries Performance Measurement Systems

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Procedia Engineering 2015年 100卷 1348-1353页

作者： Kristjan Kuhi Kati Kõrbe Kaare Ott Koppel Department of Logistics and Transport Tallinn University of Technology Akadeemia 15A 12616 Tallinn Estonia

The vast development of information and communication technologies has created new possibilities to acquire and analyze data to take performance measurement systems to next level. Most commonly performance measurement has been known as a financial management tool. Sophisticated new technologies have made it possible to collect continuous real-time data and enabled to start designing and implementing nonfinancial performance measurement systems. Most network industries are undertakings of dominant position and therefore subjects to strict supervision. For the authorities to fulfill their regulatory functions, precise monitoring and systemized feedback on the performance of network industries is essential. The problem lies in non-complete data in terms of missing, faulty or delayed values which might lead to incorrect management decisions. The objective of this paper is to explore the use of mathematical models for missing data prediction in performance measurement systems. Applying deterministic models hide the uncertainty of the value state therefore with higher likelihood false diagnoses occur. Authors propose probabilistic models because likelihood based methods for missing data calculation are able to take into account different parameters and time aspect in a single model to convey more trustworthy estimates in performance measurement systems than traditional methods.

关键词： industrial engineering performance measurement data collection probabilistic graphical models

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Improving structure MCMC for Bayesian networks through Markov blanket resampling

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Chengwei Su Mark E. Borsuk Google MPI for Intelligent Systems Thayer School of Engineering Dartmouth College Hanover NH

Algorithms for inferring the structure of Bayesian networks from data have become an increasingly popular method for uncovering the direct and indirect influences among variables in complex systems. A Bayesian approach to structure learning uses posterior probabilities to quantify the strength with which the data and prior knowledge jointly support each possible graph feature. Existing Markov Chain Monte Carlo (MCMC) algorithms for estimating these posterior probabilities are slow in mixing and convergence, especially for large networks. We present a novel Markov blanket resampling (MBR) scheme that intermittently reconstructs the Markov blanket of nodes, thus allowing the sampler to more effectively traverse low-probability regions between local maxima. As we can derive the complementary forward and backward directions of the MBR proposal distribution, the Metropolis-Hastings algorithm can be used to account for any asymmetries in these proposals. Experiments across a range of network sizes show that the MBR scheme outperforms other state-of-the-art algorithms, both in terms of learning performance and convergence rate. In particular, MBR achieves better learning performance than the other algorithms when the number of observations is relatively small and faster convergence when the number of variables in the network is large.

关键词： Bayesian inference Markov chain Monte Carlo directed acyclic graph probabilistic graphical models

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Temporal Hierarchical Adaptive Texture CRF for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2015年第6期34卷 1227-1241页

作者： Karimaghaloo, Zahra Rivaz, Hassan Arnold, Douglas L. Collins, D. Louis Arbel, Tal McGill Univ Ctr Intelligent Machines Montreal PQ H3A 2A7 Canada Concordia Univ Elect & Comp Engn Dept Montreal PQ H3G 1M8 Canada Concordia Univ PERFORM Ctr Montreal PQ H3G 1M8 Canada NeuroRx Res Montreal PQ H2X 3P9 Canada McGill Univ Montreal Neurol Inst Montreal PQ H3A 2B4 Canada

We propose a conditional random field (CRF) based classifier for segmentation of small enhanced pathologies. Specifically, we develop a temporal hierarchical adaptive texture CRF (THAT-CRF) and apply it to the challenging problem of gad enhancing lesion segmentation in brain MRI of patients with multiple sclerosis. In this context, the presence of many nonlesion enhancements (such as blood vessels) renders the problem more difficult. In addition to voxel-wise features, the framework exploits multiple higher order textures to discriminate the true lesional enhancements from the pool of other enhancements. Since lesional enhancements show more variation over time as compared to the nonlesional ones, we incorporate temporal texture analysis in order to study the textures of enhanced candidates over time. The parameters of the THAT-CRF model are learned based on 2380 scans from a multi-center clinical trial. The effect of different components of the model is extensively evaluated on 120 scans from a separate multi-center clinical trial. The incorporation of the temporal textures results in a general decrease of the false discovery rate. Specifically, THAT-CRF achieves overall sensitivity of 95% along with false discovery rate of 20% and average false positive count of 0.5 lesions per scan. The sensitivity of the temporal method to the trained time interval is further investigated on five different intervals of 69 patients. Moreover, superior performance is achieved by the reviewed labelings of our model compared to the fully manual labeling when applied to the context of separating different treatment arms in a real clinical trial.

关键词： Automatic segmentation magnetic resonance imaging (MRI) multiple sclerosis (MS) probabilistic graphical models

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Concurrent filtering and smoothing: A parallel architecture for real-time navigation and full smoothing

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INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2014年第12期33卷 1544-1568页

作者： Williams, Stephen Indelman, Vadim Kaess, Michael Roberts, Richard Leonard, John J. Dellaert, Frank Georgia Inst Technol Inst Robot & Intelligent Machines Atlanta GA 30332 USA Carnegie Mellon Univ Sch Comp Sci Field Robot Ctr Inst Robot Pittsburgh PA 15213 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

We present a parallelized navigation architecture that is capable of running in real-time and incorporating long-term loop closure constraints while producing the optimal Bayesian solution. This architecture splits the inference problem into a low-latency update that incorporates new measurements using just the most recent states (filter), and a high-latency update that is capable of closing long loops and smooths using all past states (smoother). This architecture employs the probabilistic graphical models of factor graphs, which allows the low-latency inference and high-latency inference to be viewed as sub-operations of a single optimization performed within a single graphical model. A specific factorization of the full joint density is employed that allows the different inference operations to be performed asynchronously while still recovering the optimal solution produced by a full batch optimization. Due to the real-time, asynchronous nature of this algorithm, updates to the state estimates from the high-latency smoother will naturally be delayed until the smoother calculations have completed. This architecture has been tested within a simulated aerial environment and on real data collected from an autonomous ground vehicle. In all cases, the concurrent architecture is shown to recover the full batch solution, even while updated state estimates are produced in real-time.

关键词： Information fusion smoothing filtering real time navigation SLAM probabilistic graphical models

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Learning perceptually grounded word meanings from unaligned parallel data

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MACHINE LEARNING 2014年第2期94卷 151-167页

作者： Tellex, Stefanie Thaker, Pratiksha Joseph, Joshua Roy, Nicholas 32 Vassar St Cambridge MA 02139 USA

In order for robots to effectively understand natural language commands, they must be able to acquire meaning representations that can be mapped to perceptual features in the external world. Previous approaches to learning these grounded meaning representations require detailed annotations at training time. In this paper, we present an approach to grounded language acquisition which is capable of jointly learning a policy for following natural language commands such as "Pick up the tire pallet," as well as a mapping between specific phrases in the language and aspects of the external world;for example the mapping between the words "the tire pallet" and a specific object in the environment. Our approach assumes a parametric form for the policy that the robot uses to choose actions in response to a natural language command that factors based on the structure of the language. We use a gradient method to optimize model parameters. Our evaluation demonstrates the effectiveness of the model on a corpus of commands given to a robotic forklift by untrained users.

关键词： Robotics Language Machine learning probabilistic graphical models

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3D segmentation of abdominal CT imagery with graphical models, conditional random fields and learning

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MACHINE VISION AND APPLICATIONS 2014年第2期25卷 301-325页

作者： Bhole, Chetan Pal, Christopher Rim, David Wismueller, Axel Univ Rochester Dept Comp Sci Rochester NY 14627 USA Ecole Polytech Dept Genie Informat & Genie Logiciel Montreal PQ H3C 3A7 Canada Univ Rochester Med Ctr Dept Imaging Sci Computat Radiol Lab Rochester NY 14642 USA Univ Munich Dept Radiol Munich Germany

probabilistic graphical models have had a tremendous impact in machine learning and approaches based on energy function minimization via techniques such as graph cuts are now widely used in image segmentation. However, the free parameters in energy function-based segmentation techniques are often set by hand or using heuristic techniques. In this paper, we explore parameter learning in detail. We show how probabilistic graphical models can be used for segmentation problems to illustrate Markov random fields (MRFs), their discriminative counterparts conditional random fields (CRFs) as well as kernel CRFs. We discuss the relationships between energy function formulations, MRFs, CRFs, hybrids based on graphical models and their relationships to key techniques for inference and learning. We then explore a series of novel 3D graphical models and present a series of detailed experiments comparing and contrasting different approaches for the complete volumetric segmentation of multiple organs within computed tomography imagery of the abdominal region. Further, we show how these modeling techniques can be combined with state of the art image features based on histograms of oriented gradients to increase segmentation performance. We explore a wide variety of modeling choices, discuss the importance and relationships between inference and learning techniques and present experiments using different levels of user interaction. We go on to explore a novel approach to the challenging and important problem of adrenal gland segmentation. We present a 3D CRF formulation and compare with a novel 3D sparse kernel CRF approach we call a relevance vector random field. The method yields state of the art performance and avoids the need to discretize or cluster input features. We believe our work is the first to provide quantitative comparisons between traditional MRFs with edge-modulated interaction potentials and CRFs for multi-organ abdominal segmentation and the first to explore the 3D

关键词： Segmentation Machine learning probabilistic graphical models Random fields Adrenal gland Abdomen Graph-cuts

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A Scientific Inquiry Fusion Theory for High-Level Information Fusion 17

A Scientific Inquiry Fusion Theory for High-Level Informatio...

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17th International Conference on Information Fusion (FUSION)

作者： Ao, Zhuoyun Scholz, Jason Oxenham, Martin Joint and Operations Analysis Division Defence Science Technology Organisation South Australia Australia

ISBN: (纸本)9781479916344

The Joint Directors of Laboratories fusion model is adequate as a functional description, but falls short as a formal design guide for the application of logical inference under uncertainty for high-level information fusion. We propose a formal construct called the Scientific Inquiry Fusion Theory, with three stages of explanation, prediction and generalisation aligned with the corresponding inferences of abduction, deduction and induction. We first define fusion as formal models without uncertainty, in which the corresponding logical inference patterns can be used for solving fusion problems. Then, we extend these formal models with uncertainty through probabilistic graphical models, where fusion processes are realised by statistical queries and learning algorithms based on the sound unification of Probability Theory, Mathematical Logic and Machine Learning. Finally, we demonstrate the application of this formal high-level information fusion framework with an example of automated maritime security situation and threat assessment.

关键词： statistical relational learning frameworks for multi-level fusion processes situation and intent assessment behaviour classification probabilistic graphical models logic-based fusion

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People on Drugs: Credibility of User Statements in Health Communities 14

People on Drugs: Credibility of User Statements in Health Co...

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20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Mukherjee, Subhabrata Weikum, Gerhard Danescu-Niculescu-Mizil, Cristian Max Planck Inst Informat Saarbrucken Germany Max Planck Inst Software Syst Saarbrucken Germany

ISBN: (纸本)9781450329569

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs-this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information.

关键词： Credibility Trustworthiness Objectivity Veracity probabilistic graphical models

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