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Region and constellations based categorization of images with unsupervised graph learning

IMAGE AND VISION COMPUTING 2009年第7期27卷 960-978页

作者： Lozano, M. A. Escolano, F. Bonev, B. Suau, P. Aguilar, W. Saez, J. M. Cazorla, M. A. Univ Alicante Dpto Ciencia Computac & Inteligencia Artificial E-03080 Alicante Spain Univ Nacl Autonoma Mexico Inst Invest Matemat Aplicadas & Sistemas Mexico City 04510 DF Mexico

in this paper, we address the problem of image categorization with a fast novel method based on the unsupervised clustering of graphs in the context of both region-based segmentation and the constellation approach to object recognition. Such method is an em central clustering algorithm which builds prototypical graphs on the basis of either Softassign or fast matching with graph transformations. We present two realistic applications and their experimental results: categorization of image segmentations and visual localization. We compare our graph prototypes with the set median graphs. Our results reveal that, on the one hand, structure extracted from images improves appearance-based visual localization accuracy. On the other hand, we show that the cost of our central graph clustering algorithm is the cost of a pairwise algorithm. We also discuss how the method scales with an increasing amount of images. In addition, we address the scientific question of what are the bounds of structural learning for categorization. Our in-depth experiments both for region-based and feature-based image categorization, will show that such bounds depend hardly on structural variability. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Image categorization Clustering of graphs em algorithms

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Understanding the variability of speed distributions under mixed traffic conditions caused by holiday traffic

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TRANSPORTATION RESEARCH PART C-emERGING TECHNOLOGIES 2010年第4期18卷 599-610页

作者： Jun, Jungwook Virginia Dept Transportat Traff Engn Div Richmond VA 23219 USA

Understanding the variability of speed patterns and congestion characteristics of interstate freeway systems caused by holiday traffic is beneficial because appropriate countermeasures for safety improvement and congestion mitigation can be prepared and drivers can avoid traffic congestion and change their holiday travel schedules. This study evaluated the traffic congestion patterns during the Thanksgiving holiday period in 2006 using a Gaussian mixture speed distribution estimated by the Expectation-Maximization (em) algorithm. This mathematical approach showed the potential of improving freeway operational performance evaluation schemes for holiday periods (even non-holiday periods). This study suggested that a Gaussian mixture model using the em algorithm could be used to properly characterize the severity and the variability of congestion on certain interstate roadway systems. However, this study also pointed out that the fundamental limitations of the mixture model and the statistical significance test about the mixture components should be well understood and need to be further investigated. In addition, because this study investigated the changing patterns of speed distributions with only one interstate freeway system, 1-95 northbound, other freeway systems with both directions need to be evaluated so that a more broad and confident analysis on holiday traffic can be achieved. Published by Elsevier Ltd.

关键词： Speed distributions Gaussian mixture models em algorithms Holiday traffic Congestions Bimodal distributions

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An introduction to hidden Markov models and Bayesian networks

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2001年第1期15卷 9-42页

作者： Ghahramani, Z UCL Gatsby Computat Neurosci Unit London WC1N 3AR England

We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective makes it possible to consider novel generalizations of hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usually intractable, one can use approximate inference algorithms such as Markov chain sampling and variational methods. We describe how such methods are applied to these generalized hidden Markov models. We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs.

关键词： dynamic Bayesian networks hidden Markov models state space models em algorithms variational methods Gibbs sampling

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Mixtures of regressions with predictor-dependent mixing proportions

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2010年第10期54卷 2253-2266页

作者： Young, D. S. Hunter, D. R. Penn State Univ Dept Stat University Pk PA 16802 USA

We extend the standard mixture of linear regressions model by allowing the mixing proportions to be modeled nonparametrically as a function of the predictors. This framework allows for more flexibility in the modeling of the mixing proportions than the fully parametric mixture of experts model, which we also discuss. We present an em-like algorithm for estimation of the new model. We also provide simulations demonstrating that our nonparametric approach can provide a better fit than the parametric approach in some instances and can serve to validate and thus reinforce the parametric approach ill others. We also analyze and interpret two real data sets using the new method. (C) 2010 Elsevier B.V. All rights reserved.

关键词： em algorithms Hierarchical mixture of experts Mixture models Mixtures of regressions

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Inventory management of remanufacturable products

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MANAGemENT SCIENCE 2000年第11期46卷 1412-1426页

作者： Toktay, LB Wein, LM Zenios, SA INSEAD Technol Management F-77305 Fontainebleau France MIT Alfred P Sloan Sch Management Cambridge MA 02139 USA Stanford Univ Grad Sch Business Stanford CA 94305 USA

We address the procurement of new components for recyclable products in the context of Kodak's single-use camera. The objective is to find an ordering policy that minimizes the total expected procurement, inventory holding, and lost sales cost. Distinguishing characteristics of the system are the uncertainty and unobservability associated with return flows of used cameras. We model the system as a closed queueing network, develop a heuristic procedure for adaptive estimation and control, and illustrate our methods with disguised data from Kodak. Using this framework, we investigate the effects of various system characteristics such as informational structure, procurement delay, demand rate, and length of the product's life cycle.

关键词： closed queueing networks production control em algorithms distributed lag model

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Iterative Smoother-Based Variance Estimation

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IEEE SIGNAL PROCESSING LETTERS 2012年第5期19卷 275-278页

作者： Einicke, G. A. Falco, G. Dunn, M. T. Reid, D. C. CSIRO Pullenvale 4069 Australia ISMB I-10138 Turin Italy

The minimum-variance smoother solution for input estimation is described and it is shown that the resulting estimates are unbiased. The smoothed input and state estimates are used to iteratively identify unknown process noise variances. The use of smoothed estimates, as opposed to filtered estimates, leads to improved approximate Cramer-Rao lower bounds for the unknown parameters. It is also shown that the sequence of iterates are monotonic and asymptotically approach the actual values under prescribed conditions. A nonlinear mining navigation application is described in which unknown parameters are estimated.

关键词： em algorithms Kalman filtering smoothing

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Large-scale estimation of random graph models with local dependence

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2020年 152卷 107029-107029页

作者： Babkin, Sergii Stewart, Jonathan R. Long, Xiaochen Schweinberger, Michael Microsoft Redmond WA USA Florida State Univ Dept Stat Tallahassee FL 32306 USA Rice Univ Dept Stat 6100 Main St Houston TX 77005 USA

A class of random graph models is considered, combining features of exponential-family models and latent structure models, with the goal of retaining the strengths of both of them while reducing the weaknesses of each of them. An open problem is how to estimate such models from large networks. A novel approach to large-scale estimation is proposed, taking advantage of the local structure of such models for the purpose of local computing. The main idea is that random graphs with local dependence can be decomposed into subgraphs, which enables parallel computing on subgraphs and suggests a two-step estimation approach. The first step estimates the local structure underlying random graphs. The second step estimates parameters given the estimated local structure of random graphs. Both steps can be implemented in parallel, which enables large-scale estimation. The advantages of the two-step estimation approach are demonstrated by simulation studies with up to 10,000 nodes and an application to a large Amazon product recommendation network with more than 10,000 products. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Exponential random graph models Latent structure models Stochastic block models Variational methods em algorithms MM algorithms

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Zero-inflated modeling part I: Traditional zero-inflated count regression models, their applications, and computational tools

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WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS 2022年第1期14卷 e1541-e1541页

作者： Young, Derek S. Roemmele, Eric S. Yeh, Peng Univ Kentucky Dr Bing Zhang Dept Stat 323 Multidisciplinary Sci Bldg Lexington KY 40536 USA Merck Res Labs Upper Gwynedd PA USA

Count regression models maintain a steadfast presence in modern applied statistics as highlighted by their usage in diverse areas like biometry, ecology, and insurance. However, a common practical problem with observed count data is the presence of excess zeros relative to the assumed count distribution. The seminal work of Lambert (1992) was one of the first articles to thoroughly treat the problem of zero-inflated count data in the presence of covariates. Since then, a vast literature has emerged regarding zero-inflated count regression models. In this first of two review articles, we survey some of the classic and contemporary literature on parametric zero-inflated count regression models, with emphasis on the utility of different univariate discrete distributions. We highlight some of the primary computational tools available for estimating and assessing the adequacy of these models. We concurrently emphasize the diverse data problems to which these models have been applied. This article is categorized under: Statistical Models > Generalized Linear Models Software for Computational Statistics > Software/Statistical Software algorithms and Computational Methods > Maximum Likelihood Methods

关键词： data dispersion em algorithms generalized linear models marginalized models mixture models statistical software

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Flow-Process Foreground Region of Interest Detection Method for Video Codecs

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IEEE ACCESS 2017年 5卷 16263-16276页

作者： Zhang, Zhewei Jing, Tao Han, Jingning Xu, Yaowu Li, Xuejing Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Google Inc Mountain View CA 94043 USA

Detecting the foreground region of interest (ROI) for video sequences is an important issue both for video codecs and monitoring systems. In this paper, we propose a flow-process-based method to detect foreground ROI using four steps: global motion compensation, motion block extraction, multi-layer segmentation, and model updating. The former two procedures extract the foreground motion blocks and form a motion mask, and the latter two procedures remove the pixels belonging to the background inside the motion mask and update the color distributions of the background model. In addition, a block-based to pixel-based detection scheme is proposed to allow detection flexibility. Another benefit of the proposed method is that it can be embedded in video codecs for real-time ROI detection and encoding. Experimental results demonstrate that our method achieves improved performance in terms of both detection accuracy and time consumption.

关键词： Foreground ROI detection video codecs image segmentation global motion detection Markov random field em algorithms machine learning

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Entropy based probabilistic collaborative clustering

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PATTERN RECOGNITION 2017年 72卷 144-157页

作者： Sublime, Jeremie Matei, Basarab Cabanes, Guenael Grozavu, Nistor Bennani, Younes Cornuejols, Antoine ISEP RDI Team LISITE Lab 10 Rue Vanves F-92130 Issy Les Moulineaux France Univ Paris 13 Sorbonne Paris Cite LIPN CNRSUMR 7030 99 Ave JB Clement F-93430 Villetaneuse France Univ Paris Saclay INRA AgroParisTech UMR MIA Paris F-75005 Paris France

Unsupervised machine learning approaches involving several clustering algorithms working together to tackle difficult data sets are a recent area of research with a large number of applications such as clustering of distributed data, multi-expert clustering, multi-scale clustering analysis or multi-view clustering. Most of these frameworks can be regrouped under the umbrella of collaborative clustering, the aim of which is to reveal the common underlying structures found by the different algorithms while analyzing the data. Within this context, the purpose of this article is to propose a collaborative framework lifting the limitations of many of the previously proposed methods: Our proposed collaborative learning method makes possible for a wide range of clustering algorithms from different families to work together based solely on their clustering solutions, thus lifting previous limitation requiring identical prototypes between the different collaborators. Our proposed framework uses a variational em as its theoretical basis for the collaboration process and can be applied to any of the previously mentioned collaborative contexts. In this article, we give the main ideas and theoretical foundations of our method, and we demonstrate its effectiveness in a series of experiments on real data sets as well as data sets from the literature. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Collaborative clustering em algorithms Entropy based methods

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