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Structure learning for weighted networks based on bayesian nonparametric models

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2016年第3期7卷 479-489页

作者： Jiang, Xiaojuan Zhang, Wensheng Univ Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

With the increase of availability and scope of complex networks, structure learning for networks has received an enormous amount of interest in many fields, including physics, computer and information sciences, biology and the social sciences. To extract compact and flexible representations for weighted networks, we propose a new bayesian nonparametric model to learn from both the existence and weight of interactions between nodes. Our model adopts Dirichlet process prior to automatically infer the partition over nodes in weighted networks without specifying the number of clusters. This is vital for structure discovery in complex networks, especially for novel domains where we have little prior knowledge. We develop a mean-field variational algorithm to efficiently approximate the model's posterior distribution over infinite latent clusters. Conducting extensive experiments on synthetic data set and four popular data sets, we demonstrate that our model can effectively capture the latent structure for complex weighted networks.

关键词： Structure learning Clustering Probabilistic graph models bayesian nonparametric models Variational inference

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Hierarchical bayesian nonparametric models for knowledge discovery from electronic medical records

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KNOWLEDGE-BASED SYSTEMS 2016年第0期99卷 168-182页

作者： Li, Cheng Rana, Santu Dinh Phung Venkatesh, Svetha Deakin Univ Ctr Pattern Recognit & Data Analyt Geelong Vic 3217 Australia

Electronic Medical Record (EMR) has established itself as a valuable resource for large scale analysis of health data. A hospital EMR dataset typically consists of medical records of hospitalized patients. A medical record contains diagnostic information (diagnosis codes), procedures performed (procedure codes) and admission details. Traditional topic models, such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet process (HDP), can be employed to discover disease topics from EMR data by treating patients as documents and diagnosis codes as words. This topic modeling helps to understand the constitution of patient diseases and offers a tool for better planning of treatment. In this paper, we propose a novel and flexible hierarchical bayesian nonparametric model, the word distance dependent Chinese restaurant franchise (wddCRF), which incorporates word-to-word distances to discover semantically-coherent disease topics. We are motivated by the fact that diagnosis codes are connected in the form of ICD-10 tree structure which presents semantic relationships between codes. We exploit a decay function to incorporate distances between words at the bottom level of wddCRF. Efficient inference is derived for the wddCRF by using MCMC technique. Furthermore, since procedure codes are often correlated with diagnosis codes, we develop the correspondence wddCRF (Corr-wddCRF) to explore conditional relationships of procedure codes for a given disease pattern. Efficient collapsed Gibbs sampling is derived for the Corr-wddCRF. We evaluate the proposed models on two real-world medical datasets - PolyVascular disease and Acute Myocardial Infarction disease. We demonstrate that the Corr-wddCRF model discovers more coherent topics than the Corr-HDP. We also use disease topic proportions as new features and show that using features from the Corr-wddCRF outperforms the baselines on 14-days readmission prediction. Beside these, the prediction for procedure codes based on the C

关键词： bayesian nonparametric models Correspondence models Word distances Disease topics Readmission prediction Procedure codes prediction

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Tracking clusters and anomalies in evolving data streams

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STATISTICAL ANALYSIS AND DATA MINING-AN ASA DATA SCIENCE JOURNAL 2022年第2期15卷 156-178页

作者： Guggilam, Sreelekha Chandola, Varun Patra, Abani Univ Buffalo State Univ New York SUNY Computat Data Sci & Engn Buffalo NY 14260 USA Univ Buffalo State Univ New York SUNY Comp Sci & Engn Buffalo NY USA Tufts Univ Data Intens Studies Ctr Medford MA 02155 USA

Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is invariably needed to identify data instances with high (or low) scores as anomalies. This presents a practical limitation on the applicability of such methods, since most methods are sensitive to the choice of the threshold, and it is challenging to set optimal thresholds. The issue is exacerbated in a streaming scenario, where the optimal thresholds vary with time. We present a probabilistic framework to explicitly model the normal and anomalous behaviors and probabilistically reason about the data. An extreme value theory based formulation is proposed to model the anomalous behavior as the extremes of the normal behavior. As a specific instantiation, a joint nonparametric clustering and anomaly detection algorithm (INCAD) is proposed that models the normal behavior as a Dirichlet process mixture model. Results on a variety of datasets, including streaming data, show that the proposed method provides effective and simultaneous clustering and anomaly detection without requiring strong initialization and threshold parameters.

关键词： anomaly detection bayesian nonparametric models clustering-based anomaly detection evolving stream data extreme value theory

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The Attraction Indian Buffet Distribution

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bayesian ANALYSIS 2022年第3期17卷 931-967页

作者： Warr, Richard L. Dahl, David B. Meyer, Jeremy M. Lui, Arthur Brigham Young Univ Dept Stat 2152 WVB Provo UT 84602 USA Los Alamos Natl Lab Stat Sci Grp Los Alamos NM 87545 USA

We propose the attraction Indian buffet distribution (AIBD), a distribution for binary feature matrices influenced by pairwise similarity information. Binary feature matrices are used in bayesian models to uncover latent variables (i.e., features) that explain observed data. The Indian buffet process (IBP) is a popular exchangeable prior distribution for latent feature matrices. In the presence of additional information, however, the exchangeability assumption is not reasonable or desirable. The AIBD can incorporate pairwise similarity information, yet it preserves many properties of the IBP, including the distribution of the total number of features. Thus, much of the interpretation and intuition that one has for the IBP directly carries over to the AIBD. A temperature parameter controls the degree to which the similarity information affects feature-sharing between observations. Unlike other nonexchangeable distributions for feature allocations, the probability mass function of the AIBD has a tractable normalizing constant, making posterior inference on hyperparameters straight-forward using standard MCMC methods. A novel posterior sampling algorithm is proposed for the IBP and the AIBD. We demonstrate the feasibility of the AIBD as a prior distribution in feature allocation models and compare the performance of competing methods in simulations and an application.

关键词： bayesian nonparametric models Chinese restaurant process clustering feature allocations Indian buffet process latent feature models

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A bayesian nonparametric mixture model for studying universal patterns in color naming

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APPLIED MATHEMATICS AND COMPUTATION 2021年 395卷 125868-125868页

作者： Joe, Kirbi Gooyabadi, Maryam Univ Calif Irvine Inst Math Behav Sci Irvine CA 92697 USA

Variational Inference for the Beta-Bernoulli Dirichlet Process Mixture Model is employed to uncover universal patterns in color naming systems. The data used consist of 2552 participants from 106 World Color Survey languages. To study these languages collectively, the model is informed by universal biological, linguistic, and topological features of the task. We find that the majority of the naming systems are represented by eighteen clusters, each constituting a universal pattern. Novel mathematical techniques are developed to study the levels of similarity, underlying consensus, and diversity among these patterns. This implementation of nonparametric models demonstrates how machine learning methods can be tailored for behavioral science applications. (c) 2020 Elsevier Inc. All rights reserved.

关键词： bayesian nonparametric models Machine learning Color naming Universality

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Supervised nonparametric Multimodal Topic Modeling Methods for Multi-class Video Classification 18

Supervised Nonparametric Multimodal Topic Modeling Methods f...

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8th ACM International Conference on Multimedia Retrieval (ACM ICMR)

作者： Xue, Jianfei Eguchi, Koji Kobe Univ Grad Sch Syst Informat Kobe Hyogo Japan

ISBN: (纸本)9781450350464

nonparametric topic models such as hierarchical Dirichlet processes (HDP) have been attracting more and more attentions for multimedia data analysis. However, the existing models for multimedia data are unsupervised ones that purely cluster semantically or characteristically related features into a specific latent topic without considering side information such as class information. In this paper, we present a novel supervised sequential symmetric correspondence HDP (Sup-SSC-HDP) model for multi-class video classification, where the empirical topic frequencies learned from multimodal video data are modeled as a predictor of video class. Qualitative and quantitative assessments demonstrate the effectiveness of Sup-SSC-HDP.

关键词： bayesian nonparametric models multimedia content classification hierarchical Dirichlet processes topic models

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Social Network-Based Content Delivery in Device-to-Device Underlay Cellular Networks Using Matching Theory

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IEEE ACCESS 2017年 5卷 924-937页

作者： Xu, Chen Gao, Caixia Zhou, Zhenyu Chang, Zheng Jia, Yunjian North China Elect Power Univ Sch Elect & Elect Engn State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla 40014 Finland Chongqing Univ Coll Commun Engn Chongqing 400044 Peoples R China

With the popularity of social network-based services, the unprecedented growth of mobile date traffic has brought a heavy burden on the traditional cellular networks. Device-to-device (D2D) communication, as a promising solution to overcome wireless spectrum crisis, can enable fast content delivery based on user activities in social networks. In this paper, we address the content delivery problem related to optimization of peer discovery and resource allocation by combining both the social and physical layer information in D2D underlay networks. The social relationship, which is modeled as the probability of selecting similar contents and estimated by using the bayesian nonparametric models, is used as a weight to characterize the impact of social features on D2D pair formation and content sharing. Next, we propose a 3-D iterative matching algorithm to maximize the sum rate of D2D pairs weighted by the intensity of social relationships while guaranteeing the quality of service requirements of both cellular and D2D links simultaneously. Moreover, we prove that the proposed algorithm converges to a stable matching and is weak Pareto optimal, and also provide the theoretical complexity. Simulation results show that the algorithm is able to achieve more than 90% of the optimum performance with a computation complexity 1000 times lower than the exhaustive matching algorithm. It is also demonstrated that the satisfaction performance of D2D receivers can be increased significantly by incorporating social relationships into the resource allocation design.

关键词： Social network device-to-device communication content delivery bayesian nonparametric models matching theory

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bayesian nonparametric DISCLOSURE RISK ESTIMATION VIA MIXED EFFECTS LOG-LINEAR models

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ANNALS OF APPLIED STATISTICS 2015年第1期9卷 525-546页

作者： Carota, Cinzia Filippone, Maurizio Leombruni, Roberto Polettini, Silvia Univ Turin Dipartimento Econ & Stat I-10153 Turin Italy Univ Glasgow Sch Comp Sci Glasgow G12 8QQ Lanark Scotland Univ Roma La Sapienza Dipartimento Metodi & Modelli Econ Terr & Finanza I-00161 Rome Italy

Statistical agencies and other institutions collect data under the promise to protect the confidentiality of respondents. When releasing microdata samples, the risk that records can be identified must be assessed. To this aim, a widely adopted approach is to isolate categorical variables key to the identification and analyze multi-way contingency tables of such variables. Common disclosure risk measures focus on sample unique cells in these tables and adopt parametric log-linear models as the standard statistical tools for the problem. Such models often have to deal with large and extremely sparse tables that pose a number of challenges to risk estimation. This paper proposes to overcome these problems by studying nonparametric alternatives based on Dirichlet process random effects. The main finding is that the inclusion of such random effects allows us to reduce considerably the number of fixed effects required to achieve reliable risk estimates. This is studied on applications to real data, suggesting, in particular, that our mixed models with main effects only produce roughly equivalent estimates compared to the all two-way interactions models, and are effective in defusing potential shortcomings of traditional log-linear models. This paper adopts a fully bayesian approach that accounts for all sources of uncertainty, including that about the population frequencies, and supplies unconditional (posterior) variances and credible intervals.

关键词： bayesian nonparametric models confidentiality disclosure risk Dirichlet process log-linear models mixed effects models

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Joint Estimation of Quantile Planes Over Arbitrary Predictor Spaces

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2017年第519期112卷 1107-1120页

作者： Yang, Yun Tokdar, Surya T. Duke Univ Stat Sci Box 90251 Durham NC 27708 USA

In spite of the recent surge of interest in quantile regression, joint estimation of linear quantile planes remains a great challenge in statistics and econometrics. We propose a novel parameterization that characterizes any collection of noncrossing quantile planes over arbitrarily shaped convex predictor domains in any dimension by means of unconstrained scalar, vector and function valued parameters. Statistical models based on this parameterization inherit a fast computation of the likelihood function, enabling penalized likelihood or bayesian approaches to model fitting. We introduce a complete bayesian methodology by using Gaussian process prior distributions on the function valued parameters and develop a robust and efficient Markov chain Monte Carlo parameter estimation. The resulting method is shown to offer posterior consistency under mild tail and regularity conditions. We present several illustrative examples where the new method is compared against existing approaches and is found to offer better accuracy, coverage and model fit. Supplementary materials for this article are available online.

关键词： bayesian inference bayesian nonparametric models Gaussian processes Joint quantile model Linear quantile regression

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Data clustering using side information dependent Chinese restaurant processes

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KNOWLEDGE AND INFORMATION SYSTEMS 2016年第2期47卷 463-488页

作者： Li, Cheng Rana, Santu Phung, Dinh Venkatesh, Svetha Deakin Univ Ctr Pattern Recognit & Data Analyt Geelong Vic 3216 Australia

Side information, or auxiliary information associated with documents or image content, provides hints for clustering. We propose a new model, side information dependent Chinese restaurant process, which exploits side information in a bayesian nonparametric model to improve data clustering. We introduce side information into the framework of distance dependent Chinese restaurant process using a robust decay function to handle noisy side information. The threshold parameter of the decay function is updated automatically in the Gibbs sampling process. A fast inference algorithm is proposed. We evaluate our approach on four datasets: Cora, 20 Newsgroups, NUS-WIDE and one medical dataset. Types of side information explored in this paper include citations, authors, tags, keywords and auxiliary clinical information. The comparison with the state-of-the-art approaches based on standard performance measures (NMI, F1) clearly shows the superiority of our approach.

关键词： Side information Similarity Data clustering bayesian nonparametric models

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