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7th Chinese Conference on Pattern Recognition (CCPR)

作者： Cheng, Qiang Chen, Li Xing, Yuanjian Yang, Yuhao Nanjing Res Inst Elect Technol Nanjing 210039 Jiangsu Peoples R China CETC Key Lab IntelliSense Technol Nanjing 210039 Jiangsu Peoples R China

ISBN: (纸本)9789811030024;9789811030017

We consider the problem of finding the M assignments with the maximum probabilities (or equivalently, the M-best MAP assignments) on a probabilistic graphical model. The covering graph approximation method provides an upper bound on each of the true M-best MAP costs. However, the tightness of these bounds is closely related to how to split the parameters of the duplicate nodes. We propose a monotonic algorithm to tighten the M-best MAP bounds by finding the optimal splitting of these parameters. Experimental results on synthetic and real problems show that our algorithm provides much tighter bounds than those provided by uniformly splitting the parameters.

关键词： M-best MAP inference probabilistic graphical models Covering graph

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probabilistic Bag-Of-Hyperlinks Model for Entity Linking 16

Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

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25th International Conference on World Wide Web (WWW)

作者： Ganea, Octavian-Eugen Ganea, Marina Lucchi, Aurelien Eickhoff, Carsten Hofmann, Thomas Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Google Inc Mountain View CA USA

ISBN: (纸本)9781450341431

Many fundamental problems in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text understanding, automatic summarization, semantic search or machine translation. Name ambiguity, word polysemy, context dependencies and a heavy-tailed distribution of entities contribute to the complexity of this problem. We here propose a probabilistic approach that makes use of an effective graphical model to perform collective entity disambiguation. Input mentions (i.e., linkable token spans) are disambiguated jointly across an entire document by combining a document-level prior of entity co-occurrences with local information captured from mentions and their surrounding context. The model is based on simple sufficient statistics extracted from data, thus relying on few parameters to be learned. Our method does not require extensive feature engineering, nor an expensive training procedure. We use loopy belief propagation to perform approximate inference. The low complexity of our model makes this step sufficiently fast for real-time usage. We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing stateof-the-art methods.

关键词： Entity linking Entity disambiguation Wikification probabilistic graphical models Approximate inference Loopy belief propagation

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Labeling abnormalities in video based complex Human-Object Interactions by robust affordance modelling

Labeling abnormalities in video based complex Human-Object I...

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International Conference on Computer Vision and Image Analysis Applications (ICCVIA)

作者： Hassan, Mahmudul Dharmaratne, Anuja Monash Univ Sch Informat Technol Subang Jaya Malaysia

ISBN: (纸本)9781479971862

Identifying abnormalities in complex Human Object Interaction (HOI) based videos and labeling their possible categories is a novel and ambitious research problem, which requires an optimal blend of the state of the art computer vision and machine learning algorithms. For classifying a HOI event normal or abnormal and subsequently classifying the potential abnormal categories requires the knowledge of the mutual relations between the Human, object and the ambient environment. Researchers have been using various contexts like spatial, temporal, sequential etc. to classify the abnormal actions. In this paper, we have introduced a novel context of object's affordance (which is a semantic map of the human, object and the ambient environment) to identify abnormalities in Human Object Interactions. Furthermore, the sub-classification of the abnormalities is also realized. In order to achieve our goal, we have introduced a set of novel attributes associated with the Human and the Objects and mapped them in a Bayesian network framework. The inference capabilities of the system depict the successful identification of abnormal events. We have also initiated a novel dataset of abnormal Human-Object Interactions in domestic settings. This research work also made a valiant effort to capitalize the abundant statistical data sources currently available, related to the domestic accidents and use them to nourish a practical classifier.

关键词： Human Object Interaction Object's affordance Dynamic time Warping (DTW) probabilistic graphical models Bayesian Network

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A formalized, integrated and visual approach to stress testing

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RISK MANAGEMENT-JOURNAL OF RISK CRISIS AND DISASTER 2016年第4期18卷 189-216页

作者： Denev, Alexander Mutnikas, Yaacov IHS Markit London England

In this paper, we will give for the first time a formal mathematical language to the steps used currently by financial institutions when calculating the impact of a stress scenario on a balance sheet that depends on more granular or different factors than those provided in the scenario. We will introduce the language of probabilistic graphical models a technique rooted in machine learning to show how the different models used at each step can be put together in a coherent picture, thus giving a holistic view of the entire model setup. This will give us a solid basis to discuss some weaknesses and problems with the stress-testing exercises run by the industry as of today. We will show empirical analyses to substantiate better some of our claims.

关键词： stress testing scenario analysis integrated risk management probabilistic graphical models visualization

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Error AMP Chain Graphs

Error AMP Chain Graphs

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12th Scandinavian Conference on Artificial Intelligence (SCAI)

作者： Pena, Jose M. Linkoping Univ IDA ADIT SE-58183 Linkoping Sweden

ISBN: (纸本)9781614993308;9781614993292

Any regular Gaussian probability distribution that can be represented by an AMP chain graph (CG) can be expressed as a system of linear equations with correlated errors whose structure depends on the CG. However, the CG represents the errors implicitly, as no nodes in the CG correspond to the errors. We propose in this paper to add some deterministic nodes to the CG in order to represent the errors explicitly. We call the result an EAMP CG. We will show that, as desired, every AMP CG is Markov equivalent to its corresponding EAMP CG under marginalization of the error nodes. We will also show that every EAMP CG under marginalization of the error nodes is Markov equivalent to some LWF CG under marginalization of the error nodes, and that the latter is Markov equivalent to some directed and acyclic graph (DAG) under marginalization of the error nodes and conditioning on some selection nodes. This is important because it implies that the independence model represented by an AMP CG can be accounted for by some data generating process that is partially observed and has selection bias. Finally, we will show that EAMP CGs are closed under marginalization. This is a desirable feature because it guarantees parsimonious models under marginalization.

关键词： probabilistic graphical models directed and acyclic graphs chain graphs

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Spectral-Spatial Neural Networks And probabilistic Graph models For Hyperspectral Image Classification

Spectral-Spatial Neural Networks And Probabilistic Graph Mod...

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作者： Zilong Zhong University of Waterloo

学位级别：博士

Pixel-wise hyperspectral image (HSI) classification has been actively studied since it shares similar characteristics with related computer vision tasks, including image classification, object detection, and semantic segmentation, but also possesses inherent differences. The research surrounding HSI classification sheds light on an approach to bridge computer vision and remote sensing. Modern deep neural networks dominate and repeatedly set new records in all image recognition challenges, largely due to their excellence in extracting discriminative features through multi-layer nonlinear transformation. However, three challenges hinder the direct adoption of convolutional neural networks (CNNs) for HSI classification. First, typical HSIs contain hundreds of spectral channels that encode abundant pixel-wise spectral information, leading to the curse of dimensionality. Second, HSIs usually have relatively small numbers of annotated pixels for training along with large numbers of unlabeled pixels, resulting in the problem of generalization. Third, the scarcity of annotations and the complexity of HSI data induce noisy classification maps, which are a common issue in various types of remotely sensed data interpretation. Recent studies show that taking the data attributes into the designing of fundamental components of deep neural networks can improve their representational capacity and then facilitates these models to achieve better recognition performance. To the best of our knowledge, no research has exploited this finding or proposed corresponding models for supervised HSI classification given enough labeled HSI data. In cases of limited labeled HSI samples for training, conditional random fields (CRFs) are an effective graph model to impose data-agnostic constraints upon the intermediate outputs of trained discriminators. Although CRFs have been widely used to enhance HSI classification performance, the integration of deep learning and probabilistic graph models in t

关键词： hyperspectral image classification spectral-spatial neural networks generative adversarial networks probabilistic graphical models

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A Multi-agent Decision Process for Controlling Heating, Ventilation, and Air-conditioning Systems

A Multi-agent Decision Process for Controlling Heating, Vent...

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作者： Windham, Andrew Winsome PennState University Libraries

学位级别：Doctor of Philosophy

The primary focus of this work is to explore and describe a proposed decision process for use in controlling heating, ventilation, and air conditioning systems in buildings. The larger problem that houses the decision process is structured as a multi-agent building control system. This is because multi-agent systems are an effective way to incorporate new sources of external and sporadic information into the basic system. In this study, sources of occupant information are used to demonstrate the decision process. The decision process begins with agents formulating context specific opinions using a method that is derived from Bayesian Networks and probabilistic graphical modeling. From there, agents convey their preferences using conditional game theory, setting up the group of agents for action. While there are a variety of action procedures that could be used, the process was developed with Nash equilibriums in mind. The Nash equilibriums, however, were not yet successfully implemented due to the early stages of the social influence modeling. Instead, a basic social welfare function was employed to demonstrate the decision process for a setpoint decision context. A three zone, variable air volume simulator was developed in MATLAB to represent a physical environment and to act as a reference case for typical PI control. The simulator is then used for exploring and describing the decision process implementation, which is run in parallel to the basic system simulator. The Bayesian Network-based opinion formulation method developed by this work holds promise and has been found to merit further development. A component of the conditional game structure, rejectability, was not found to be a natural context for use in the setpoint decision context, but it may be improved upon. Overall, systems integration was desired and appears possible by implementing the decision process.

关键词： Building Automation Multi-Agent Systems Conditional Game Theory HVAC probabilistic graphical models Distributed Control

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Sensor Registration and Tracking from Heterogeneous Sensors with Belief Propagation 22

Sensor Registration and Tracking from Heterogeneous Sensors ...

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22nd International Conference on Information Fusion (FUSION)

作者： Cormack, David Hopgood, James R. Heriot Watt Univ Sch Engn & Phys Sci Edinburgh EH14 4AS Midlothian Scotland Univ Edinburgh Inst Digital Commun Edinburgh EH9 3FG Midlothian Scotland

ISBN: (纸本)9780996452786

Modern sensor platforms carry an increasingly diverse range of sensors onboard, in order to estimate target positions inside their common surveillance region. Off-the-shelf sensors often provide measurements at different rates, and with different and potentially varying levels of uncertainty. Heterogeneous and asynchronous sensor networks make the sensor fusion problem more challenging as multiple measurement models and a dynamic prediction model are required. Moreover, a key challenge to address in fusion systems is that of sensor bias. Any relative bias between sensors could result in measurements not correlating with one another in a common frame of reference, and therefore vastly reducing track accuracy. This work presents novel results on a joint method that estimates both external angular bias between a radar and a camera, and the states of multiple targets. The proposed technique uses a particle-based implementation of Belief Propagation (BP), and compares with Random Finite Set (RFS)-based approaches. Initial results show that the BP approach outperforms the RFS approaches in terms of accuracy by around 50% when using the Optimal Sub-Pattern Assignment (OSPA) metric.

关键词： sensor registration probabilistic graphical models radar camera target tracking data fusion

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DISCRIMINATIVE graphical models FOR SPARSITY-BASED HYPERSPECTRAL TARGET DETECTION

DISCRIMINATIVE GRAPHICAL MODELS FOR SPARSITY-BASED HYPERSPEC...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Srinivas, Umamahesh Chen, Yi Monga, Vishal Nasrabadi, Nasser M. Tran, Trac D. Penn State Univ Dept Elect Engn University Pk PA 16802 USA

ISBN: (纸本)9781467311595

The inherent discriminative capability of sparse representations has been exploited recently for hyperspectral target detection. This approach relies on the observation that the spectral signature of a pixel can be represented as a linear combination of a few training spectra drawn from both target and background classes. The sparse representation corresponding to a given test spectrum captures class-specific discriminative information crucial for detection tasks. Spatio-spectral information has also been introduced into this framework via a joint sparsity model that simultaneously solves for the sparse features for a group of spatially local pixels, since such pixels are highly likely to have similar spectral characteristics. In this paper, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between these distinct sparse representations corresponding to different pixels in a spatial neighborhood. Simulation results show that the proposed algorithm outperforms classical hyperspectral target detection algorithms as well as support vector machines.

关键词： Hyperspectral target detection sparsity probabilistic graphical models

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External Consistency Maintenance Algorithm for Chain and Stellate Structures of Algebraic Bayesian Networks: Statistical Experiments for Running Time Analysis 3rd

External Consistency Maintenance Algorithm for Chain and Ste...

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3rd International Scientific Conference on Intelligent Information Technologies for Industry (IITI)

作者： Kharitonov, Nikita Malchevskaia, Ekaterina Zolotin, Andrey Abramov, Maksim Russian Acad Sci St Petersburg Inst Informat & Automat 3914 Line Vasilyevsky Ostrov St Petersburg 199178 Russia St Petersburg State Univ St Petersburg Russia

ISBN: (纸本)9783030018214

This article describes an experiment demonstrating the running time of the algorithm for maintainance of external consistency in algebraic Bayesian networks. In the experiment, the stellate and chain structures of algebraic Bayesian networks are compared. The results of the experiment demonstrate the dependency of the algorithm complexity on the number of atoms in the network, as well as on the intersections and in the fragments of knowledge.

关键词： probabilistic graphical models Algebraic bayesian networks Knowledge patterns Global probabilistic-logic inference Maintenance of external consistency Statistical experiments

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