2018 IEEE International Conference on Big Knowledge (ICBK)最新文献

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Deep Co-Investment Network Learning for Financial Assets 金融资产深度共同投资网络学习

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-09-12 DOI: 10.1109/ICBK.2018.00014

Yue Wang, Chenwei Zhang, Shen Wang, Philip S. Yu, Lu Bai, Lixin Cui

{"title":"Deep Co-Investment Network Learning for Financial Assets","authors":"Yue Wang, Chenwei Zhang, Shen Wang, Philip S. Yu, Lu Bai, Lixin Cui","doi":"10.1109/ICBK.2018.00014","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00014","url":null,"abstract":"Most recent works model the market structure of the stock market as a correlation network of the stocks. They apply pre-defined patterns to extract correlation information from the time series of stocks. Without considering the influences of the evolving market structure to the market index trends, these methods hardly obtain the market structure models which are compatible with the market principles. Advancements in deep learning have shown their incredible modeling capacity on various finance-related tasks. However, the learned inner parameters, which capture the essence of the finance time series, are not further exploited about their representation in the financial fields. In this work, we model the financial market structure as a deep co-investment network and propose a Deep Co-investment Network Learning (DeepCNL) method. DeepCNL automatically learns deep co-investment patterns between any pairwise stocks, where the rise-fall trends of the market index are used for distance supervision. The learned inner parameters of the trained DeepCNL, which encodes the temporal dynamics of deep co-investment patterns, are used to build the co-investment network between the stocks as the investment structure of the corresponding market. We verify the effectiveness of DeepCNL on the real-world stock data and compare it with the existing methods on several financial tasks. The experimental results show that DeepCNL not only has the ability to better reflect the stock market structure that is consistent with widely-acknowledged financial principles but also is more capable to approximate the investment activities which lead to the stock performance reported in the real news or research reports than other alternatives.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133805745","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

Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks 异构信息网络中元路径与元图的联合嵌入

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-09-11 DOI: 10.1109/ICBK.2018.00025

Lichao Sun, Lifang He, Zhipeng Huang, Bokai Cao, Congying Xia, Xiaokai Wei, Philip S. Yu

引用次数: 24

An Out-of-the-box Full-Network Embedding for Convolutional Neural Networks 卷积神经网络的开箱即用全网络嵌入

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2017-05-22 DOI: 10.1109/ICBK.2018.00030

D. García-Gasulla, Armand Vilalta, Ferran Par'es, Jonatan Moreno, Eduard Ayguad'e, Jesús Labarta, Ulises Cort'es, T. Suzumura

{"title":"An Out-of-the-box Full-Network Embedding for Convolutional Neural Networks","authors":"D. García-Gasulla, Armand Vilalta, Ferran Par'es, Jonatan Moreno, Eduard Ayguad'e, Jesús Labarta, Ulises Cort'es, T. Suzumura","doi":"10.1109/ICBK.2018.00030","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00030","url":null,"abstract":"Features extracted through transfer learning can be used to exploit deep learning representations in contexts where there are very few training samples, where there are limited computational resources, or when the tuning of hyper-parameters needed for training deep neural networks is unfeasible. In this paper we propose a novel feature extraction embedding called full-network embedding. This embedding is based on two main points. First, the use of all layers of the network, integrating activations from different levels of information and from different types of layers (ie convolutional and fully connected). Second, the contextualisation and leverage of information based on a novel three-valued discretisation method. The former provides extra information useful to extend the characterisation of data, while the later reduces noise and regularises the embedding space. Significantly, this also reduces the computational cost of processing the resultant representations. The proposed method is shown to outperform single layer embeddings on several image classification tasks, while also being more robust to the choice of the pre-trained model used as the transfer source.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129627682","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: 17

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