2018 IEEE International Conference on Big Knowledge (ICBK)最新文献_第3页

Decomposing TripAdvisor: Detecting Potentially Fraudulent Hotel Reviews in the Era of Big Data 分解TripAdvisor:在大数据时代发现潜在的欺诈性酒店评论

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00040

Christopher G. Harris

引用次数: 7

Risk Factor Analysis of Bone Mineral Density Based on Feature Selection in Type 2 Diabetes 基于特征选择的2型糖尿病患者骨密度危险因素分析

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00037

Wei Wang, Bingbing Jiang, S. Ye, Liting Qian

引用次数: 4

Discriminative Graph Autoencoder 判别图自动编码器

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00033

Haifeng Jin, Qingquan Song, X. Hu

{"title":"Discriminative Graph Autoencoder","authors":"Haifeng Jin, Qingquan Song, X. Hu","doi":"10.1109/ICBK.2018.00033","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00033","url":null,"abstract":"With the abundance of graph-structured data in various applications, graph representation learning has become an effective computational tool for seeking informative vector representations for graphs. Traditional graph kernel approaches are usually frequency-based. Each dimension of a learned vector representation for a graph is the frequency of a certain type of substructure. They encounter high computational cost for counting the occurrence of predefined substructures. The learned vector representations are very sparse, which prohibit the use of inner products. Moreover, the learned vector representations are not in a smooth space since the values can only be integers. The state-of-the-art approaches tackle the challenges by changing kernel functions instead of producing better vector representations. They can only produce kernel matrices for kernel-based methods and not compatible with methods requiring vector representations. Effectively learning smooth vector representations for graphs of various structures and sizes remains a challenging task. Motivated by the recent advances in deep autoencoders, in this paper, we explore the capability of autoencoder on learning representations for graphs. Unlike videos or images, the graphs are usually of various sizes and are not readily prepared for autoencoder. Therefore, a novel framework, namely discriminative graph autoencoder (DGA), is proposed to learn low-dimensional vector representations for graphs. The algorithm decomposes the large graphs into small subgraphs, from which the structural information is sampled. The DGA produces smooth and informative vector representations of graphs efficiently while preserving the discriminative information according to their labels. Extensive experiments have been conducted to evaluate DGA. The experimental results demonstrate the efficiency and effectiveness of DGA comparing with traditional and state-of-the-art approaches on various real-world datasets and applications, e.g., classification and visualization.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"518 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134011960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Neural Networks for Predicting the Output of wind flow Simulations Over Complex Topographies 在复杂地形上预测风流模拟输出的神经网络

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00032

Michael Mayo, S. Wakes, C. Anderson

引用次数: 3

Biometric Recognition Through Eye Movements Using a Recurrent Neural Network 利用递归神经网络通过眼球运动进行生物特征识别

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00016

Shaohua Jia, D. Koh, Amanda Seccia, Pasha Antonenko, Richard L. Lamb, Andreas Keil, M. Schneps, M. Pomplun

引用次数: 18

On the Learning Capabilities of Recurrent Neural Networks: A Cryptographic Perspective 递归神经网络的学习能力:一个密码学的视角

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00029

Shivin Srivastava, Ashutosh Bhatia

引用次数: 4

Learning Discrimination Specific, Self-Collaborative and Nonlinear Model 学习分辨特异性、自协作和非线性模型

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00048

Dimche Kostadinov, Behrooz Razeghi, S. Voloshynovskiy, Sohrab Ferdowsi

引用次数: 1

Multi-label Learning Based on Label-Specific Feature Extraction 基于标签特征提取的多标签学习

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00047

Ting Nie

引用次数: 0

Automatic Detection of Warped Patterns in Time Series: The Caterpillar Algorithm 时间序列扭曲模式的自动检测:卡特彼勒算法

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00063

Maximilian Leodolter, Norbert Brändle, C. Plant

引用次数: 2

DMTMV: A Unified Learning Framework for Deep Multi-task Multi-view Learning DMTMV:深度多任务多视图学习的统一学习框架

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00015

Yi-Feng Wu, De-chuan Zhan, Yuan Jiang

{"title":"DMTMV: A Unified Learning Framework for Deep Multi-task Multi-view Learning","authors":"Yi-Feng Wu, De-chuan Zhan, Yuan Jiang","doi":"10.1109/ICBK.2018.00015","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00015","url":null,"abstract":"As the development of data collection techniques, complicated objects are described with more than one aspects as well as possess multiple concepts, so as many data mining approaches face the issues of dual-heterogeneity, i.e., feature heterogeneity and task heterogeneity. Traditional multi-task learning methods and multi-view learning methods may be not optimal for such a complicated learning problem since they only capture one type of heterogeneity. Then some works concentrate on a new direction where there are multiple related tasks with multi-view data. However, most existing MTMV methods focus on proposing linear models for fitting the specific application requirements while they are not suitable for common large-scale real-world problems in real environments. In this paper, we propose a unified learning framework for a deep multi-task multi-view neural network. In our approach, there are three kinds of networks called shared feature network, specific feature network and task network, each of which focus on the feature heterogeneity, unified feature representations, and task heterogeneity, respectively. Meanwhile, we employ a layer-by-layer regularization strategy for learning the relationships between tasks in multi-task multi-view learning. Moreover, the DMTMV method is naturally convenient for multi-class heterogeneous tasks as well. Finally, experiments on four real-world datasets successfully show that the proposed framework can significantly improve the prediction performance in multi-task multi-view learning while it can also discover inherent relationships among different tasks.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129975370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4