Exploring Groups from Heterogeneous Data via Sparse Learning

机译：通过稀疏学习从异构数据中探索群体

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摘要

Complexity networks, such as social networks, biological networks and co-citation networks, are ubiquitous in reality. Identifying groups from data is critical for network analysis, for it can offer deep insights in understanding the structural properties and functions of complex networks. Over the past decades, many endeavors from interdisciplinary fields have been attempted to identify groups from data. However, little attention has been paid on exploring groups and their relationships from different views. In this work, we address this issue by using canonical correlation analysis (CCA) to analyze groups and their interplays in the networks. To further improve the interpretability of results, we solve the optimization problem with sparse learning, and then propose a generalized framework of group discovery from heterogeneous data. This framework enables us to find groups and explicitly model their relationships from diverse views simultaneously. Extensive experimental studies conducted on both synthetic and DBLP datasets demonstrate the effectiveness of the proposed method.

机译：实际上，诸如社交网络，生物网络和共引网络之类的复杂性网络无处不在。从数据中识别组对于网络分析至关重要，因为它可以为理解复杂网络的结构特性和功能提供深刻的见解。在过去的几十年中，已经尝试了跨学科领域的许多努力来从数据中识别群体。然而，很少有人关注从不同角度探讨群体及其关系。在这项工作中，我们通过使用规范相关分析（CCA）分析网络中的组及其相互作用来解决此问题。为了进一步提高结果的可解释性，我们用稀疏学习解决了优化问题，然后提出了一种从异构数据中发现群体的通用框架。这个框架使我们能够找到团体，并同时从不同的观点明确地建立他们之间的关系模型。在合成数据集和DBLP数据集上进行的大量实验研究证明了该方法的有效性。

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来源
《Advances in knowledge discovery and data mining》|2013年|556-567|共12页
会议地点 Gold Coast(AU)
作者
Huawen Liu; Jiuyong Li; Lin Liu; Jixue Liu; Ivan Lee; Jianmin Zhao;
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作者单位

Zhejiang Normal University, Jinhua 321004, China,Academy of Mathematics and Systems Science, CAS, Beijing 100190, China;

University of South Australia, Adelaide, SA5095, Australia;

University of South Australia, Adelaide, SA5095, Australia;

University of South Australia, Adelaide, SA5095, Australia;

University of South Australia, Adelaide, SA5095, Australia;

Zhejiang Normal University, Jinhua 321004, China;

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原文格式 PDF
正文语种 eng
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关键词
Group discovery; complexity network; canonical correlation analysis; LASSO; Sparse learning;

机译：小组发现；复杂性网络；典型相关分析；套索;稀疏学习;

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1. Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning [J] . Wang Hua, Nie Feiping, Huang Heng, Bioinformatics . 2012,第12期

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2. Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging geneticsn data via sparse multimodal multitask learning [J] . Bioinformatics . 2012,第12期

机译：通过稀疏多模态多任务学习从多维异质成像遗传学数据中识别疾病敏感性和数量性状相关的生物标记
3. TREVOR HASTIE, ROBERT TIBSHIRANI, AND MARTIN WAINWRIGHT . Statistical Learning with Sparsity: The Lasso and Generalizations . Boca Raton : CRC Press . TREVOR HASTIE, ROBERT TIBSHIRANI, AND MARTIN WAINWRIGHT TREVOR HASTIE, ROBERT TIBSHIRANI, AND MARTIN WAINWRIGHT . Statistical Learning with Sparsity: The Lasso and Generalizations Statistical Learning with Sparsity: The Lasso and Generalizations . Boca Raton Boca Raton : CRC Press CRC Press . [J] . Kondofersky Ivan, Theis Fabian J. Biometrics: Journal of the Biometric Society : An International Society Devoted to the Mathematical and Statistical Aspects of Biology . 2018,第2期

机译：Trevor Hastie，Robert Tibshirani和Martin Wainwright。统计学习稀疏性：套索和概括。 Boca Raton：CRC压力机。 Trevor Hastie，Robert Tibshirani和Martin Wainwright Trevor Hastie，Robert Tibshirani和Martin Wainwright。统计学习与稀疏性：套索与稀疏性统计学习：套索和概括。 Boca Raton Boca Raton：CRC按CRC压力机。
4. Exploring Groups from Heterogeneous Data via Sparse Learning [C] . Huawen Liu, Jiuyong Li, Lin Liu, Pacific-Asia conference on advances in knowledge discovery and data mining . 2013

机译：通过稀疏学习探索来自异构数据的群组
5. Sparse and large-scale learning models and algorithms for mining heterogeneous big data. [D] . Cai, Xiao. 2013

机译：用于挖掘异构大数据的稀疏大规模学习模型和算法。
6. Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning [O] . Hua Wang, Feiping Nie, Heng Huang, -1

机译：通过稀疏多模式多任务学习从多维异质成像遗传学数据中识别疾病敏感和与数量性状相关的生物标记
7. Tackling ordinal regression problem for heterogeneous data: sparse and deep multi-task learning approaches [O] . Lu Wang, Dongxiao Zhu 2021

机译：解决异构数据的序数回归问题：稀疏和深度多任务学习方法

Exploring Groups from Heterogeneous Data via Sparse Learning

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