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19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Kuo, Tsung-Ting Yan, Rui Huang, Yu-Yang Kung, Perng-Hwa Lin, Shou-De Natl Taiwan Univ Taipei Taiwan Peking Univ Beijing Peoples R China

ISBN: (纸本)9781450321747

The concern of privacy has become an important issue for online social networks. In services such as ***, whether a person likes an article is considered private and therefore not disclosed;only the aggregative statistics of articles (i.e., how many people like this article) is revealed. This paper tries to answer a question: can we predict the opinion holder in a heterogeneous social network without any labeled data? This question can be generalized to a link prediction with aggregative statistics problem. This paper devises a novel unsupervised framework to solve this problem, including two main components: (1) a three-layer factor graph model and three types of potential functions;(2) a ranked-margin learning and inference algorithm. Finally, we evaluate our method on four diverse prediction scenarios using four datasets: preference (Foursquare), repost (Twitter), response (Plurk), and citation (DBLP). We further exploit nine unsupervised models to solve this problem as baselines. Our approach not only wins out in all scenarios, but on the average achieves 9.90% AUC and 12.59% NDCG improvement over the best competitors. The resources are available at http://***/-d97944007/aggregative/

关键词： Link prediction Social network mining Heterogeneous social network probabilistic graphical model

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Unsupervised Discovery of Drug Side-Effects from Heterogeneous Data Sources 17

Unsupervised Discovery of Drug Side-Effects from Heterogeneo...

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23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Ma, Fenglong Meng, Chuishi Xiao, Houping Li, Qi Gao, Jing Su, Lu Zhang, Aidong SUNY Buffalo Buffalo NY 14260 USA

ISBN: (纸本)9781450348874

Drug side-effects become a worldwide public health concern, which are the fourth leading cause of death in the United States. Pharmaceutical industry has paid tremendous effort to identify drug side-effects during the drug development. However, it is impossible and impractical to identify all of them. Fortunately, drug side-effects can also be reported on heterogeneous platforms (i.e., data sources), such as FDA Adverse Event Reporting System and various online communities. However, existing supervised and semi-supervised approaches are not practical as annotating labels are expensive in the medical field. In this paper, we propose a novel and effective unsupervised model Sifter to automatically discover drug side-effects. Sifter enhances the estimation on drug side-effects by learning from various online platforms and measuring platform-level and user-level quality simultaneously. In this way, Sifter demonstrates better performance compared with existing approaches in terms of correctly identifying drug side-effects. Experimental results on five real-world datasets show that Sifter can significantly improve the performance of identifying side-effects compared with the state-of-the-art approaches.

关键词： Healthcare informatics truth discovery probabilistic graphical model drug side-effects

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Object Recognition in Images via a Factor Graph model 9

Object Recognition in Images via a Factor Graph Model

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9th International Conference on Graphic and Image Processing (ICGIP)

作者： He, Yong Wang, Long Wu, Zhaolin Zhang, Haisu Natl Univ Def Technol Wuhan 430010 Hubei Peoples R China

ISBN: (数字)9781510617421

ISBN: (纸本)9781510617421

Object recognition in images suffered from huge search space and uncertain object profile. Recently, the Bag-of-Words methods are utilized to solve these problems, especially the 2-dimension CRF(Conditional Random Field) model. In this paper we suggest the method based on a general and flexible fact graph model, which can catch the long-range correlation in Bag-of-Words by constructing a network learning framework contrasted from lattice in CRF. Furthermore, we explore a parameter learning algorithm based on the gradient descent and Loopy Sum-Product algorithms for the factor graph model. Experimental results on Graz 02 dataset show that, the recognition performance of our method in precision and recall is better than a state-of-art method and the original CRF model, demonstrating the effectiveness of the proposed method.

关键词： Factor Graph Object Recognition probabilistic graphical model

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Document Clustering using Mixture model of von Mises-Fisher Distributions on Document Manifold

Document Clustering using Mixture Model of von Mises-Fisher ...

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International Conference of Soft Computing and Pattern Recognition (SoCPaR)

作者： Nguyen Kim Anh Nguyen The Tam Ngo Van Linh Hanoi Univ Sci & Technol Hanoi Vietnam

ISBN: (纸本)9781479934003

Document clustering has become an increasingly important technique for unsupervised document organization, automatic topic extraction, and fast information retrieval or filtering. The generative model for document clustering based on the von Mises-Fisher (vMF) distribution generally produces better clustering results than other generative models. However, in fact, it is more natural and reasonable to assume that the document space is a manifold and the probability distribution that generates the data is supported on a document manifold. In this paper, we propose a regularized probabilistic model based on manifold structure for data clustering, called Laplacian regularized vMF Mixture model (LapvMFs), which explicitly considers the manifold structure. We have developed a generalized meanfield variational inference algorithm for the LapvMFs. Extensive experimental results on a large number of high dimensional text datasets demonstrate that our approach outperforms the three state-of-the-art clustering algorithms.

关键词： probabilistic graphical model variational inference clustering graph laplacian manifold

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Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes

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Engineering 2020年第4期6卷 449-461页

作者： Zhaofei Yu Jian K.Liu Shanshan Jia Yichen Zhang Yajing Zheng Yonghong Tian Tiejun Huang National Engineering Laboratory for Video Technology School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Peng Cheng Laboratory Shenzhen 518055China Center for Systems Neuroscience Department of NeurosciencePsychology and BehaviourUniversity of LeicesterLeicester LE17RHUK

A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and controlling some part of a human brain or *** visual information can be processed by the brain in millisecond *** retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further ***,the neuronal signal of interest for a retinal neuroprosthesis is the neuronal ***-loop computation in a neuroprosthesis includes two stages:encoding a stimulus as a neuronal signal,and decoding it back into a *** this paper,we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic *** hypothesize that in order to obtain a better understanding of the computational principles in the retina,a hypercircuit view of the retina is necessary,in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the *** different building blocks of the retina,which include a diversity of cell types and synaptic connections-both chemical synapses and electrical synapses(gap junctions)-make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual *** overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.

关键词： Visual coding Retina Neuroprosthesis Brain-machine interface Artificial intelligence Deep learning Spiking neural network probabilistic graphical model

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Semantic Image Retrieval Using Correspondence Topic model with Background Distribution

Semantic Image Retrieval Using Correspondence Topic Model wi...

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International Conference on Big Data and Smart Computing (BigComp)

作者： Nguyen Anh Tu Cho, Jinsung Lee, Young-Koo Kyung Hee Univ Dept Comp Engn Seoul South Korea

ISBN: (纸本)9781467387965

Social image search becomes an active research field in recent years due to the rapid development in big data processing technologies. In the retrieval systems, text description/tags play a key role to bridge the semantic gap between low-level features and higher-level concepts, and so guarantee the reliable search. However, in practice manual tags are usually noisy and incomplete, resulting in a limited performance of image retrieval. To tackle this problem, we propose a probabilistic topic model to formalize the correlation of tags with visual features via the latent semantic topics. Our proposed approach allows us to effectively annotate and refine tags based on a Monte Carlo Markov Chain algorithm for approximate inference. Moreover, we present a measuring scheme using the refined tags and extracted topics for ranking the images. The experimental results from two large benchmark datasets show that our approach provides promising accuracy.

关键词： Topic modeling probabilistic graphical model tag refinement image annotation image retrieval

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A Hardware Generator for Factor Graph Applications

A Hardware Generator for Factor Graph Applications

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作者： Demma, James Daniel Virginia Tech | University

A Factor Graph (FG -- http://***/wiki/Factor_graph) is a structure used to find solutions to problems that can be represented as a probabilistic graphical model (PGM). They consist of interconnected variable nodes and factor nodes, which iteratively compute and pass messages to each other. FGs can be applied to solve decoding of forward error correcting codes, Markov chains and Markov Random Fields, Kalman Filtering, Fourier Transforms, and even some games such as Sudoku. In this paper, a framework is presented for rapid prototyping of hardware implementations of FG-based applications. The FG developer specifies aspects of the application, such as graphical structure, factor computation, and message passing algorithm, and the framework returns a design. A system of Python scripts and Verilog Hardware Description Language templates together are used to generate the HDL source code for the application. The generated designs are vendor/platform agnostic, but currently target the Xilinx Virtex-6-based ML605. The framework has so far been primarily applied to construct Low Density Parity Check (LDPC) decoders. The characteristics of a large basket of generated LDPC decoders, including contemporary 802.11n decoders, have been examined as a verification of the system and as a demonstration of its capabilities. As a further demonstration, the framework has been applied to construct a Sudoku solver.

关键词： Factor Graph probabilistic graphical model Digital Design Sum-Product Min-Sum Belief Propagation Hardware Generator Thesis

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A point-of-interest suggestion algorithm in Multi-source geo-social networks

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 88卷

作者： Xiong, Xi Qiao, Shaojie Li, Yuanyuan Han, Nan Yuan, Guan Zhang, Yongqing Chengdu Univ Informat Technol Sch Cybersecur Chengdu 610225 Peoples R China Chengdu Univ Informat Technol Sch Software Engn Chengdu 610225 Peoples R China Sichuan Univ Mental Hlth Ctr West China Hosp Chengdu 610041 Peoples R China Sichuan Univ Sch Aeronaut & Astronaut Chengdu 610065 Peoples R China Chengdu Univ Informat Technol Sch Management Chengdu 610103 Peoples R China Guangdong Prov Engn Ctr China Made High Performan Guangdong Prov Key Lab Popular High Performance C Guangzhou 518060 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Chengdu Univ Informat Technol Sch Comp Sci Chengdu 610225 Peoples R China

Newly emerging location-based social network (LBSN) services provide us with new platforms to share interests and individual experience based on their activity history. The problems of data sparsity and user distrust in LBSNs create a severe challenge for traditional recommender systems. Moreover, users' behaviors in LBSNs show an obvious spatio-temporal pattern. Valuable extra information from microblog-based social networks (MBSNs) can be utilized to improve the effectiveness of POI suggestion. In this study, we propose a latent probabilistic generative model called MTAS, which can accurately capture the underlying information in users' words extracted from both LBSNs and MBSNs by taking into consideration the decision probability, a latent variable indicating a user's tendency to publish a review in LBSNs or MBSNs. Then, the parameters of the MTAS model can be inferred by the Gibbs sampling method in an effective manner. Based on MTAS, we design an effective framework to fulfill the top-k suggestion. Extensive experiments on two real geo-social networks show that MTAS achieves better performance than existing state-of-the-art methods.

关键词： Location-based social networks POI suggestion Geo-social networks probabilistic graphical model Gibbs sampling

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modeling Buying Motives for Personalized Product Bundle Recommendation

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2017年第3期11卷 28-28页

作者： Liu, Guannan Fu, Yanjie Chen, Guoqing Xiong, Hui Chen, Can Beihang Univ Beijing 100191 Peoples R China Missouri Univ Sci & Technol 1870 Miner Cir Rolla MO 65409 USA Tsinghua Univ Beijing 100084 Peoples R China Rutgers State Univ 1 Washington Pk Newark NJ 07029 USA

Product bundling is a marketing strategy that offers several products/items for sale as one bundle. While the bundling strategy has been widely used, less efforts have been made to understand how items should be bundled with respect to consumers' preferences and buying motives for product bundles. This article investigates the relationships between the items that are bought together within a product bundle. To that end, each purchased product bundle is formulated as a bundle graph with items as nodes and the associations between pairs of items in the bundle as edges. The relationships between items can be analyzed by the formation of edges in bundle graphs, which can be attributed to the associations of feature aspects. Then, a probabilistic model BPM (Bundle Purchases with Motives) is proposed to capture the composition of each bundle graph, with two latent factors node-type and edge-type introduced to describe the feature aspects and relationships respectively. Furthermore, based on the preferences inferred from the model, an approach for recommending items to form product bundles is developed by estimating the probability that a consumer would buy an associative item together with the item already bought in the shopping cart. Finally, experimental results on real-world transaction data collected from well-known shopping sites show the effectiveness advantages of the proposed approach over other baseline methods. Moreover, the experiments also show that the proposed model can explain consumers' buying motives for product bundles in terms of different node-types and edge-types.

关键词： Product bundle recommendation buying motives probabilistic graphical model

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A model BASED ON INFLUENCE DIAGRAMS FOR MULTI-CRITERIA DECISION-MAKING

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2012年第4期21卷

作者： Sedki, Karima Delcroix, Veronique Univ Rennes 1 UMR 6074 IRISA Agrocampus Ouset F-35042 Rennes France Univ Lille Nord France F-59000 Lille France LAMIH UMR CNRS 8201 UVHC F-59313 Valenciennes France

In this paper, we focus on multi-criteria decision-making problems. We propose a model based on influence diagrams;this model is able to handle uncertainty, represent interdependencies among the different decision variables and facilitate communication between the decision-maker and the analyst. The particular structure of the proposed model makes it possible to take into account the alternatives described by an attribute set, the decision-maker's characteristics and preferences, and other information (e.g., internal or external factors) that influence the decision. modeling the decision problem in terms of influence diagrams requires a lot of work to gather expert knowledge. However, once the model is built, it can be easily and efficiently used for different instances of the decision problem. Infact, using our model simply requires entering some basic information, such as the values of internal or external factors and the decision-maker's characteristics. Our model also defines the importance of each criterion in terms of what is known about the decision maker, the quality index and the utility of each alternative.

关键词： Influence diagram decision multi-criteria decision making probabilistic graphical model

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