检索结果-内蒙古大学图书馆

probabilistic Multigraph modeling for Improving the Quality of Crowdsourced Affective Data

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2019年第1期10卷 115-128页

作者： Ye, Jianbo Li, Jia Newman, Michelle G. Adams, Reginald B., Jr. Wang, James Z. Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA Penn State Univ Dept Stat University Pk PA 16802 USA Penn State Univ Dept Psychol University Pk PA 16802 USA

We proposed a probabilistic approach to joint modeling of participants' reliability and humans' regularity in crowdsourced affective studies. Reliability measures how likely a subject will respond to a question seriously;and regularity measures how often a human will agree with other seriously-entered responses coming from a targeted population. Crowdsourcing-based studies or experiments, which rely on human self-reported affect, pose additional challenges as compared with typical crowdsourcing studies that attempt to acquire concrete non-affective labels of objects. The reliability of participants has been massively pursued for typical non-affective crowdsourcing studies, whereas the regularity of humans in an affective experiment in its own right has not been thoroughly considered. It has been often observed that different individuals exhibit different feelings on the same test question, which does not have a sole correct response in the first place. High reliability of responses from one individual thus cannot conclusively result in high consensus across individuals. Instead, globally testing consensus of a population is of interest to investigators. Built upon the agreement multigraph among tasks and workers, our probabilistic model differentiates subject regularity from population reliability. We demonstrate the method's effectiveness for in-depth robust analysis of large-scale crowdsourced affective data, including emotion and aesthetic assessments collected by presenting visual stimuli to human subjects.

关键词： Emotions human subjects crowdsourcing probabilistic graphical model visual stimuli

来源：评论

学校读者我要写书评

暂无评论

Affective Impression: Sentiment-Awareness POI Suggestion via Embedding in Heterogeneous LBSNs

引用

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2022年第1期13卷 272-284页

作者： Xiong, Xi Qiao, Shaojie Han, Nan Li, Yuanyuan Xiong, Fei He, Ling Chengdu Univ Informat Technol Chengdu 610225 Peoples R China Chengdu Univ Informat Technol Sch Software Engn Chengdu 610225 Peoples R China Chengdu Univ Informat Technol Software Automat Generat & Intelligent Serv Key L Chengdu 610225 Sichuan Peoples R China Chengdu Univ Informat Technol Sch Management Chengdu 610103 Sichuan Peoples R China Sichuan Univ Mental Hlth Ctr West China Sch Med Chengdu 610041 Sichuan Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Sichuan Univ Coll Elect Engn & Informat Technol Chengdu 610065 Peoples R China

Location-based social networks (LBSNs) add geographical information into traditional social networks and link people's virtual and physical lives. As an important application of LBSNs, point-of-interest (POI) suggestion has become an important method to help users explore interesting and attractive locations in LBSNs. The main problems of POI suggestion include data sparsity and cold start, which have been paid much attention by existing techniques. There are two major challenges which can greatly influence the performance of suggestion accuracy. One is the fuzzy boundary between sentiments, i.e., the fine distinction between sentiments makes it difficult to classify words and texts after word-sentiment mapping operation. The other challenge is the unreliability of data quality represented by similarity metrics, which relies on data integrity and path reachability of a heterogeneous network. To cope with the above two challenges, we present a novel framework called Community-based Sentiment Extraction and Network Embedding for POI Recommendation (CENTER) for suggesting impressive POIs to a specific user in an effective fashion. The CENTER framework contains two essential techniques: (1) a latent probabilistic generative model called Community-based Sentiment Extraction (CSE), which can accurately capture the sentiments from review content in LBSNs by taking into consideration the characteristics of social communities. The parameters of the CSE model can be inferred effectively by the Gibbs sampling method. The primary sentiments are obtained based on the distribution of sentiments;(2) a network embedding model called Sentiment-aware Nework Embedding for POI Recommendation (SNER) is employed to learn the representation of the factors including POIs, users and textual sentiments in a low-dimensional embedding space. The joint training is utilized to alternatively sample all sets of edges in a heterogeneous information network. Extensive experiments were conducted on t

关键词： Network embedding affective computing location-based social networks POI suggestion heterogeneous networks probabilistic graphical model

来源：评论

学校读者我要写书评

暂无评论

A tree conditional random field model for panel detection in comic images

引用

PATTERN RECOGNITION 2015年第7期48卷 2129-2140页

作者： Li, Luyuan Wang, Yongtao Suen, Ching Y. Tang, Zhi Liu, Dong Peking Univ Beijing 100871 Peoples R China Concordia Univ Montreal PQ H3G 1M8 Canada

The goal of panel detection is to decompose the comic image into several panels (or frames), which is the fundamental step to produce digital comic books that are suitable for reading on mobile devices. The existing methods are limited in presenting the extracted panels as squares or rectangles and solely use one type of visual patterns, which are not generic in terms of handling comic images with multiple styles or complex layouts. To overcome the shortcomings of the existing approaches, we propose a novel method to detect panels within comic images. The method incorporates three types of visual patterns extracted from the comic image at different levels and a tree conditional random field framework is used to label each visual pattern by modeling its contextual dependencies. The final panel detection results are obtained by the visual pattern labels and a post-processing stage. Notably, the detected panels are presented as convex polygons in order to keep their content integrity. Experimental results demonstrate that the proposed method achieves better performance than the existing ones. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Comic image document Panel detection probabilistic graphical model Conditional random field Context modeling

来源：评论

学校读者我要写书评

暂无评论

modelling-based joint embedding of histology and genomics using canonical correlation analysis for breast cancer survival prediction

引用

ARTIFICIAL INTELLIGENCE IN MEDICINE 2024年 149卷 102787-102787页

作者： Subramanian, Vaishnavi Syeda-Mahmood, Tanveer Do, Minh N. Univ Illinois Elect & Comp Engn Urbana IL 61801 USA IBM Res Almaden San Jose CA 95120 USA

Traditional approaches to predicting breast cancer patients' survival outcomes were based on clinical subgroups, the PAM50 genes, or the histological tissue's evaluation. With the growth of multi -modality datasets capturing diverse information (such as genomics, histology, radiology and clinical data) about the same cancer, information can be integrated using advanced tools and have improved survival prediction. These methods implicitly exploit the key observation that different modalities originate from the same cancer source and jointly provide a complete picture of the cancer. In this work, we investigate the benefits of explicitly modelling multi -modality data as originating from the same cancer under a probabilistic framework. Specifically, we consider histology and genomics as two modalities originating from the same breast cancer under a probabilistic graphical model (PGM). We construct maximum likelihood estimates of the PGM parameters based on canonical correlation analysis (CCA) and then infer the underlying properties of the cancer patient, such as survival. Equivalently, we construct CCA-based joint embeddings of the two modalities and input them to a learnable predictor. Real -world properties of sparsity and graph -structures are captured in the penalized variants of CCA (pCCA) and are better suited for cancer applications. For generating richer multi -dimensional embeddings with pCCA, we introduce two novel embedding schemes that encourage orthogonality to generate more informative embeddings. The efficacy of our proposed prediction pipeline is first demonstrated via low prediction errors of the hidden variable and the generation of informative embeddings on simulated data. When applied to breast cancer histology and RNA -sequencing expression data from The Cancer Genome Atlas (TCGA), our model can provide survival predictions with average concordance -indices of up to 68.32% along with interpretability. We also illustrate how the pCCA embeddings ca

关键词： Multi-modality Canonical correlation analysis Breast cancer Survival prediction probabilistic graphical model

来源：评论

学校读者我要写书评

暂无评论

We Know Who You Are: Discovering Similar Groups Across Multiple Social Networks

引用

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第7期50卷 2693-2704页

作者： Liu, Xiaoming Shen, Chao Guan, Xiaohong Zhou, Yadong Xi An Jiao Tong Univ Key Lab Intelligent Networks & Network Secur Minist Educ Xian 710049 Peoples R China

People use various online social networks for different purposes. The user information on each social network is usually partial. Thus, matching the users across these multiple online social networks is of great significance for providing new services as well as new insights on user behaviors. Recent research shows that group structure widely exists on social networks, in which members work together with certain purpose and are more influential than individuals on online social networks. Previous works provide outstanding solutions for mapping individuals, but few ones pay attention to the study of groups across multiple social networks. To address the research gap, we first aim to propose an effective method to detect similar group across multiple social networks. The method mainly has three steps, including detecting group structure based on random walks, extracting similarity features, and inferring group similarity using probabilistic graphical model. We evaluate our algorithm on five different types of online social networks. Experimental results show that our method achieves 0.693, 0.779, 0.729 in precision, recall, and F1-measure, which significantly surpass the state-of-the-art by 31.4%-44.3%, 17.3%-26.3%, and 25.9%-31.6%, respectively. The outstanding performance of our method demonstrates that our proposal can reach the requirement of detecting similar groups across social networks. In particular, the result of this paper paves the way for the recommendation system, link prediction and information diffusion across sites.

关键词： Social network services Feature extraction graphical models Mathematical model probabilistic logic Scalability Cybernetics Algorithm experiments group similarity multiple online social networks probabilistic graphical model random walks

来源：评论

学校读者我要写书评

暂无评论

Context augmented Dynamic Bayesian Networks for event recognition

引用

PATTERN RECOGNITION LETTERS 2014年第Jul.1期43卷 62-70页

作者： Wang, Xiaoyang Ji, Qiang Rensselaer Polytech Inst Dept ECSE Troy NY 12180 USA

This paper proposes a new probabilistic graphical model (PGM) to incorporate the scene, event object interaction, and the event temporal contexts into Dynamic Bayesian Networks (DBNs) for event recognition in surveillance videos. We first construct the baseline event DBNs for modeling the events from their own appearance and kinematic observations, and then augment the DBN with contexts to improve its event recognition performance. Unlike the existing context methods, our model incorporates various contexts simultaneously into one unified model. Experiments on real scene surveillance datasets with complex backgrounds show that the contexts can effectively improve the event recognition performance even under great challenges like large intra-class variations and low image resolution. (C) 2013 Elsevier B. V. All rights reserved.

关键词： Context model Event recognition Dynamic Bayesian Networks Video surveillance probabilistic graphical model

来源：评论

学校读者我要写书评

暂无评论

Combining intensional with extensional query evaluation in tuple independent probabilistic databases

引用

INFORMATION SCIENCES 2011年第4期181卷 812-831页

作者： Qin, Biao Wang, Shan Renmin Univ China MOE Key Lab Data Engn & Knowledge Engn Beijing 100872 Peoples R China Renmin Univ China Sch Informat Beijing 100872 Peoples R China

In this paper, we prove that a query plan is safe in tuple independent probabilistic databases if and only if its every answer tuple is tree structured in probabilistic graphical models. We classify hierarchical queries into core and non-core hierarchical queries and show that the existing methods can only generate safe plans for core hierarchical queries. Inspired by the bucket elimination framework, we give the sufficient and necessary conditions for the answer relation of every candidate sub-query to be used as a base relation. Finally, the proposed algorithm generates safe plans for extensional query evaluation on non-boolean hierarchical queries and invokes the SPROUT algorithm [24] for intensional query evaluation on boolean queries. A case study on the TPC-H benchmark reveals that the safe plans of Q7 and Q8 can be evaluated efficiently. Furthermore, extensive experiments show that safe plans generated by the proposed algorithm scale well. (C) 2010 Elsevier Inc. All rights reserved.

关键词： probabilistic databases probabilistic graphical model Hierarchy Safe plan Bucket elimination framework

来源：评论

学校读者我要写书评

暂无评论

Multi-task Sparse Structure Learning with Gaussian Copula models

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2016年第1期17卷 1-30页

作者： Goncalves, Andre R. Von Zuben, Fernando J. Banerjee, Arindam Univ Estadual Campinas Sch Elect & Comp Engn Sao Paulo Brazil Univ Minnesota Twin Cities Dept Comp Sci Minneapolis MN USA

Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. While sometimes the underlying task relationship structure is known, often the structure needs to be estimated from data at hand. In this paper, we present a novel family of models for MTL, applicable to regression and classification problems, capable of learning the structure of tasks relationship. In particular, we consider a joint estimation problem of the tasks relationship structure and the individual task parameters, which is solved using alternating minimization. The task relationship revealed by structure learning is founded on recent advances in Gaussian graphical models endowed with sparse estimators of the precision (inverse covariance) matrix. An extension to include flexible Gaussian copula models that relaxes the Gaussian marginal assumption is also proposed. We illustrate the e ff ectiveness of the proposed model on a variety of synthetic and benchmark data sets for regression and classi fi cation. We also consider the problem of combining Earth System model (ESM) outputs for better projections of future climate, with focus on projections of temperature by combining ESMs in South and North America, and show that the proposed model outperforms several existing methods for the problem.

关键词： multi-task learning structure learning Gaussian copula probabilistic graphical model sparse modeling

来源：评论

学校读者我要写书评

暂无评论

Geometric Matrix Completion With Deep Conditional Random Fields

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020年第9期31卷 3579-3593页

作者： Nguyen, Duc Minh Calderbank, Robert Deligiannis, Nikos Vrije Univ Brussel Dept Elect & Informat B-1050 Brussels Belgium Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

The problem of completing high-dimensional matrices from a limited set of observations arises in many big data applications, especially recommender systems. The existing matrix completion models generally follow either a memory- or a model-based approach, whereas geometric matrix completion (GMC) models combine the best from both approaches. Existing deep-learning-based geometric models yield good performance, but, in order to operate, they require a fixed structure graph capturing the relationships among the users and items. This graph is typically constructed by evaluating a pre-defined similarity metric on the available observations or by using side information, e.g., user profiles. In contrast, Markov-random-fields-based models do not require a fixed structure graph but rely on handcrafted features to make predictions. When no side information is available and the number of available observations becomes very low, existing solutions are pushed to their limits. In this article, we propose a GMC approach that addresses these challenges. We consider matrix completion as a structured prediction problem in a conditional random field (CRF), which is characterized by a maximum a posteriori (MAP) inference, and we propose a deep model that predicts the missing entries by solving the MAP inference problem. The proposed model simultaneously learns the similarities among matrix entries, computes the CRF potentials, and solves the inference problem. Its training is performed in an end-to-end manner, with a method to supervise the learning of entry similarities. Comprehensive experiments demonstrate the superior performance of the proposed model compared to various state-of-the-art models on popular benchmark data sets and underline its superior capacity to deal with highly incomplete matrices.

关键词： Data models Predictive models Computational modeling Neural networks Measurement Benchmark testing Deep learning Deep conditional random field (CRF) deep learning geometric matrix completion (GMC) mean-field inference probabilistic graphical model

来源：评论

学校读者我要写书评

暂无评论

Temporal probabilistic measure for link prediction in collaborative networks

引用

APPLIED INTELLIGENCE 2017年第1期47卷 83-95页

作者： Lakshmi, T. Jaya Bhavani, S. Durga Univ Hyderabad Sch Comp & Informat Sci Hyderabad Andhra Pradesh India Vasireddy Venkatadri Inst Technol Nambur Guntur India

Link prediction addresses the problem of finding potential links that may form in the future. Existing state of art techniques exploit network topology for computing probability of future link formation. We are interested in using graphical models for link prediction. graphical models use higher order topological information underlying a graph for computing Co-occurrence probability of the nodes pertaining to missing links. Time information associated with the links plays a major role in future link formation. There have been a few measures like Time-score, Link-score and T_Flow, which utilize temporal information for link prediction. In this work, Time-score is innovatively incorporated into the graphical model framework, yielding a novel measure called Temporal Co-occurrence Probability (TCOP) for link prediction. The new measure is evaluated on four standard benchmark data sets : DBLP, Condmat, HiePh-collab and HiePh-cite network. In the case of DBLP network, TCOP improves AUROC by 12 % over neighborhood based measures and 5 % over existing temporal measures. Further, when combined in a supervised framework, TCOP gives 93 % accuracy. In the case of three other networks, TCOP achieves a significant improvement of 5 % on an average over existing temporal measures and an average of 9 % improvement over neighborhood based measures. We suggest an extension to link prediction problem called Long-term link prediction, and carry out a preliminary investigation. We find TCOP proves to be effective for long-term link prediction.

关键词： Social networks Link prediction Markov random field probabilistic graphical model Temporal measure

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：