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10th IEEE International Conference on Big Knowledge (ICBK)

作者： Wang, Zhen Fan, Maohong Muknahallipatna, Suresh Lan, Chao Univ Wyoming Dept Comp Sci Laramie WY 82071 USA Univ Wyoming Dept Chem Engn Laramie WY 82071 USA Univ Wyoming Dept Elect & Comp Engn Laramie WY 82071 USA

ISBN: (纸本)9781728146072

This paper considers anomaly detection with multiview data. Unlike traditional detection on single-view data which identifies anomalies based on inconsistency between instances, multi-view anomaly detection identifies anomalies based on view inconsistency within each instance. Current multi-view detection approaches are mostly unsupervised and transductive. This may have limited performance in many applications, which have labeled normal data and prefer efficient detection on new data. In this paper, we propose an inductive semi-supervised multi-view anomaly detection approach. We design a probabilistic generative model for normal data, which assumes different views of a normal instance are generated from a shared latent factor, conditioned on which the views become independent. We estimate the model by maximizing its likelihood on normal data using the EM algorithm. Then, we apply the model to detect anomalies, which are instances generated with small probabilities. We experiment our approach on nine public data sets under different multi-view anomaly settings, and show it outperforms several state-of-the-art multi-view detection methods.

关键词： Anomaly detection Multi-view learning probabilistic generative model Expectation Maximization

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Training and compensation of class-conditioned NMF bases for speech enhancement

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NEUROCOMPUTING 2018年 284卷 107-118页

作者： Chung, Hanwook Badeau, Roland Plourde, Eric Champagne, Benoit McGill Univ Dept Elect & Comp Engn Montreal PQ Canada Univ Paris Saclay Telecom ParisTech LTCI F-75013 Paris France Sherbrooke Univ Dept Elect & Comp Engn Sherbrooke PQ Canada

In this paper, we introduce a training and compensation algorithm of the class-conditioned basis vectors in the non-negative matrix factorization (NMF) model for single-channel speech enhancement. The main goal is to estimate the basis vectors of different signal sources in a way that prevents them from representing each other, in order to reduce the residual noise components that have features similar to the speech signal. During the proposed training stage, the basis matrices for the clean speech and noises are estimated jointly by constraining them to belong to different classes. To this end, we employ the probabilistic generative model (PGM) of classification, specified by class-conditional densities, as an a priori distribution for the basis vectors. The update rules of the NMF and the PGM parameters of classification are jointly obtained by using the variational Bayesian expectation-maximization (VBEM) algorithm, which guarantees convergence to a stationary point. Another goal of the proposed algorithm is to handle a mismatch between the characteristics of the training and test data. This is accomplished during the proposed enhancement stage, where we implement a basis compensation scheme. Specifically, we use extra free basis vectors to capture the features that are not included in the training data. Objective experimental results for different combination of speaker and noise types show that the proposed algorithm can provide better speech enhancement performance than the benchmark algorithms under various conditions. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Single-channel speech enhancement Non-negative matrix factorization probabilistic generative model Classification Variational Bayesian expectation-maximization

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A Network Embedding-Enhanced Approach for Generalized Community Detection 11th

A Network Embedding-Enhanced Approach for Generalized Commun...

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11th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： He, Dongxiao Yang, Xue Feng, Zhiyong Chen, Shizhan Fogelman-Soulie, Francoise Tianjin Univ Tianjin Key Lab Cognit Comp & Applicat Tianjin 300350 Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin 300350 Peoples R China Tianjin Univ Sch Comp Software Tianjin 300350 Peoples R China

ISBN: (纸本)9783319992471;9783319992464

Community detection is one of the most important tasks in network analysis. Many community detection methods have been proposed recently. However, they typically focus on assortative community structures (i.e. nodes within the same community have more connections), while ignoring the diversity of community patterns in real world. In addition, the network topology, which these methods are mainly based on, is often noisy and very sparse. These two issues bring difficulties to existing methods for accurately finding communities. To address these problems, we propose a new probabilistic generative model. In this model, we first use an idea of mixture modeling to describe network regularities, and then introduce network embeddings to further enhance the ability of this model to describe network communities. Based on these, the new model will not only find generalized communities (e.g. assortative communities, disassortative communities, and their mixture), but also be robust for community detection in complicated situations (e.g. on very sparse networks with large noise). We present an efficient expectation-maximization (EM) algorithm to learn the model. Finally, we demonstrate the superior performance of our new approach over some state-of-the-art methods on both synthetic and real networks, and also validate its robustness to the above issues via a case study analysis.

关键词： Community detection Generalized communities Network embedding probabilistic generative model EM algorithm

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A SUPERVISED MULTI-CHANNEL SPEECH ENHANCEMENT ALGORITHM BASED ON BAYESIAN NMF model

A SUPERVISED MULTI-CHANNEL SPEECH ENHANCEMENT ALGORITHM BASE...

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Chung, Hanwook Plourde, Eric Champagne, Benoit McGill Univ Dept Elect & Comp Engn Montreal PQ Canada Sherbrooke Univ Dept Elect & Comp Engn Sherbrooke PQ Canada

ISBN: (纸本)9781728112954

In this paper, we introduce a supervised multi-channel speech enhancement algorithm based on a Bayesian multi-channel non-negative matrix factorization (MNMF) model. In the proposed framework, we consider the probabilistic generative model (PGM) of MNMF, specified by Poisson-distributed latent variables and gamma-distributed priors. In the training stage, the MNMF parameters of the speech and noise sources are estimated via the variational Bayesian expectation-maximization (VBEM) algorithm. In the enhancement stage, the clean speech signal is estimated via the MNMF-based minimum variance distortionless response (MVDR) beamformer. To further improve the enhanced speech quality, we efficiently combine the MNMF-based beamforming technique with a classical unsupervised single-channel enhancement method. Experiments show that the proposed method can provide better enhancement performance than the selected benchmarks.

关键词： Multi-channel speech enhancement MVDR beamforming non-negative matrix factorization probabilistic generative model variational Bayesian expectation-maximization

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modeling Patient Visit Using Electronic Medical Records for Cost Profile Estimation 23rd

Modeling Patient Visit Using Electronic Medical Records for ...

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23rd International Conference on Database Systems for Advanced Applications (DASFAA).

作者： Zhao, Kangzhi Zhang, Yong Wang, Zihao Yin, Hongzhi Zhou, Xiaofang Wang, Jin Xing, Chunxiao Tsinghua Univ Inst Internet Ind Dept Comp Sci & Technol RIITTNList Beijing 100084 Peoples R China Univ Queensland Brisbane Qld Australia Univ Calif Los Angeles Comp Sci Dept Los Angeles CA USA

ISBN: (纸本)9783319914589;9783319914572

Estimating health care cost of patients provides promising opportunities for better management and treatment to medical providers and patients. Existing clinical approaches only focus on patient's demographics and historical diagnoses but ignore ample information from clinical records. In this paper, we formulate the problem of patient's cost profile estimation and use Electronic Medical Records (EMRs) to model patient visit for better estimating future health care cost. The performance of traditional learning based methods suffered from the sparseness and high dimensionality of EMR dataset. To address these challenges, we propose Patient Visit probabilistic generative model (PVPGM) to describe a patient's historical visits in EMR. With the help of PVPGM, we can not only learn a latent patient condition in a low dimensional space from sparse and missing data but also hierarchically organize the high dimensional EMR features. The model finally estimates the patient's health care cost through combining the effects learned both from the latent patient condition and the generative process of medical procedure. We evaluate the proposed model on a large collection of real-world EMR dataset with 836,033 medical visits from over 50,000 patients. Experimental results demonstrate the effectiveness of our model.

关键词： Electronic medical records Cost profile estimation Health care data mining probabilistic generative model

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Machine-Learning-Based Approaches for Learning Marketing Strategies

Machine-Learning-Based Approaches for Learning Marketing Str...

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作者： Lin, Yu San PennState University Libraries

学位级别：Doctor of Philosophy

Studying the markets for better business strategies has been a pressing and practical issue. However, there is not enough attention paid into such research field when it comes to precise computational models. In this thesis, I looked into various marketing problems via approaches including data analysis, probabilistic generative models, and neural network models. The specific research problems I looked into includes (1) analyzing the rewards on crowdfunding platforms and how they correlate with crowdfunding project success, (2) learning the offering and bundling preferences of menu bundles in crowdfunding projects, (3) modeling the market competition and capturing the businesses' competitiveness, and (4) learning the word embedding that carries both the underlying semantic meaning and economic worths. We approach each of these research problems as *** the first paper, Analysis of Rewards on Reward-Based Crowdfunding Platforms, we analyze a Kickstarter dataset, which consists of approximately 3K projects and 30K rewards. The analysis employs various statistical methods, including Pearson correlation tests, Kolmogorov-Smirnow test and Kaplan-Meier estimation, to study the relationships between various reward characteristics and project success. We find that projects with more rewards, with limited and late-added rewards are more likely to succeed. We also categorize and automatically annotate rewards into fifteen reward item types. We further analyze how different types of reward items are adopted across various categories of projects as well as how they potentially affect project success. We discover that projects with include the previous rewards are most likely to succeed. Also, different categories of projects may employ different best strategies of adopting reward item types to achieve pledging goals. Finally, we verify the efficacy of reward-related information through predicting project success. The result shows that features extracted from rewards help

关键词： machine learning deep learning probabilistic generative model marketing crowdfunding menu design competition word worth reward kickstarter

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A survey on trends of cross-media topic evolution map

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KNOWLEDGE-BASED SYSTEMS 2017年 124卷 164-175页

作者： Zhou, Houkui Yu, Huimin Hu, Roland Hu, Junguo Zhejiang Univ Dept Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China State Key Lab CAD & CG Hangzhou 310027 Zhejiang Peoples R China Zhejiang A&F Univ Sch Informat Engn Linan 311300 Peoples R China Zhejiang Prov Key Lab Forestry Intelligent Monito Linan 311300 Peoples R China

Rapid advancements in internet and social media technologies have made "information overload" a rampant and widespread problem. Complex subjects, histories, or issues break down into branches, side stories, and intertwining narratives;a "topic evolution map" can assist in joining together and clarifying these disparate parts of an unfamiliar territory. This paper reviews the extant research on topic evolution map based on text and cross-media corpora over the past decade. We first define a series of necessary terms, then go on to describe the traditional topic evolution map per 1) topic evolution over time, based on the probabilistic generative model, and 2) topic evolution from a non-probabilistic perspective. Next, we discuss the current state of research on topic evolution map based on the cross-media corpus, including some open questions and possible future research directions. The main contribution of this review is in its construction of an evolution map that can be used to visualize and integrate the extant studies on topic modeling - specifically in regards to cross-media research. (C) 2017 Published by Elsevier B.V.

关键词： Cross-media Topic evolution Topic map probabilistic generative model

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Multi-label text classification based on the label correlation mixture model

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INTELLIGENT DATA ANALYSIS 2017年第6期21卷 1371-1392页

作者： He, Zhiyang Wu, Ji Lv, Ping Tsinghua Univ Dept Elect Engn Beijing Peoples R China Tsinghua iFlytek Joint Lab Speech Technol Beijing Peoples R China

In the current paper, we propose a probabilistic generative model, the label correlation mixture model (LCMM), to depict multi-labeled document data, which can be utilized for multi-label text classification. LCMM assumes two stochastic generative processes, which correspond to two submodels: 1) a label correlation model;and 2) a label mixture model. The former model formulates labels' generative process, in which a label correlation network is created to depict the dependency between labels. Moreover, we present an efficient inference algorithm for calculating the generative probability of a multi-label class. Furthermore, in order to optimize the label correlation network, we propose a parameter-learning algorithm based on gradient descent. The second submodel in the LCMM depicts the generative process of words in a document with the given labels. Different traditional mixture models can be adopted in this generative process, such as the mixture of language models, or topic models. In the multi-label classification stage, we propose a two-step strategy to most efficiently utilize the LCMM based on the framework of Bayes decision theory. We conduct extensive multi-label classification experiments on three standard text data sets. The experimental results show significant performance improvements comparing to existing approaches. For example, the improvements on accuracy and macro F-score measures in the OHSUMED data set achieve 28.3% and 37.0%, respectively. These performance enhancements demonstrate the effectiveness of the proposed models and solutions.

关键词： Label correlation mixture model probabilistic generative model multi-label text classification label correlation model label correlation network Bayes decision theory

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Bayesian body schema estimation using tactile information obtained through coordinated random movements

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ADVANCED ROBOTICS 2017年第3期31卷 118-134页

作者： Mimura, Tomohiro Hagiwara, Yoshinobu Taniguchi, Tadahiro Inamura, Tetsunari Ritsumeikan Univ Grad Sch Sci & Engn Kusatsu Japan Natl Inst Informat Principles Informat Res Div Tokyo Japan Grad Univ Adv Studies Principles Informat Res Div Tokyo Japan

This paper describes a computational model, called the Dirichlet process Gaussian mixture model with latent joints (DPGMM-LJ), that can find latent tree structure embedded in data distribution in an unsupervised manner. By combining DPGMM-LJ and a preexisting body map formation method, we propose a method that enables an agent having multilink body structure to discover its kinematic structure, i.e. body schema, from tactile information alone. The DPGMM-LJ is a probabilistic model based on Bayesian nonparametrics and an extension of Dirichlet process Gaussian mixture model (DPGMM). In a simulation experiment, we used a simple fetus model that had five body parts and performed structured random movements in a womb-like environment. It was shown that the method could estimate the number of body parts and kinematic structures without any preexisting knowledge in many cases. Another experiment showed that the degree of motor coordination in random movements affects the result of body schema formation strongly. It is confirmed that the accuracy rate for body schema estimation had the highest value 84.6% when the ratio of motor coordination was 0.9 in our setting. These results suggest that kinematic structure can be estimated from tactile information obtained by a fetus moving randomly in a womb without any visual information, even though its accuracy was not so high. They also suggest that a certain degree of motor coordination in random movements and the sufficient dimension of state space that represents the body map are important to estimate body schema correctly.

关键词： Body schema body map probabilistic generative model Bayesian nonparametrics general movements

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Towards complex activity recognition using a Bayesian network-based probabilistic generative framework

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PATTERN RECOGNITION 2017年 68卷 295-309页

作者： Liu, Li Wang, Shu Su, Guoxin Huang, Zi-Gang Liu, Ming Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Chongqing Univ Sch Software Engn Chongqing 400044 Peoples R China Southwest Univ Fac Mat & Energy Chongqing 400715 Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW 2522 Australia Lanzhou Univ Sch Phys Sci & Technol Lanzhou 730000 Peoples R China Southwest Univ Fac Comp & Informat Sci Chongqing 400715 Peoples R China

Complex activity recognition is challenging since a complex activity can be performed in different ways, with each having its own configuration of primitive events and their temporal dependencies. To address such temporal relational variabilities in complex activity recognition, we propose a Bayesian network based probabilistic generative framework that employs Allen's interval relation network to represent local temporal dependencies in a generative way. By employing the Chinese restaurant process and introducing relation generation constraints, our framework can characterize these unique internal configurations of a particular complex activity as a joint distribution. Three concrete models are implemented based on our framework. Specifically, in this paper we improve two of our previous models and provide an enhanced model to handle temporal relational variabilities in complex activities more efficiently. Empirical evaluations on three benchmark datasets demonstrate the competitiveness of our framework. In particular, it is shown that our models are rather robust against errors caused by the low-level predictions from raw signals. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Activity recognition Bayesian network Complex activity probabilistic generative model Temporal relation Uncertainty

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