复杂交互的耦合学习
Coupling Learning of Complex Interactions
摘要：
大数据分析等复杂应用涉及不同形式的耦合关系，这些关系反映了与技术、业务（特定领域）和环境（包括社会文化和经济）方面相关的因素之间的相互作用。在结构不良和结构不良的数据中嵌入了各种形式的耦合。这样的耦合是普遍存在的、隐含的和/或明确的、客观的和/或主观的、异构的和/或同质的，从而向统计学、数学和计算机科学中的现有学习系统呈现复杂性，诸如典型的依赖性、关联和相关关系。因此，建模和学习这种耦合是基本的，但具有挑战性。本文讨论了耦合学习的概念，重点是在学习系统中的耦合关系的参与。耦合学习在深入理解业务问题的本质和处理现有学习理论和工具尚未解决的挑战方面具有巨大的潜力。这一论点是验证了耦合学习的几个案例研究，包括处理耦合推荐系统，将耦合到耦合聚类，耦合文档聚类，耦合推荐算法和耦合行为分析的群体。
英文摘要：
Complex applications such as big data analytics involve different forms of coupling relationships that reflect interactions between factors related to technical, business (domain-specific) and environmental (including socio-cultural and economic) aspects. There are diverse forms of couplings embedded in poor-structured and ill-structured data. Such couplings are ubiquitous, implicit and/or explicit, objective and/or subjective, heterogeneous and/or homogeneous, presenting complexities to existing learning systems in statistics, mathematics and computer sciences, such as typical dependency, association and correlation relationships. Modeling and learning such couplings thus is fundamental but challenging. This paper discusses the concept of coupling learning, focusing on the involvement of coupling relationships in learning systems. Coupling learning has great potential for building a deep understanding of the essence of business problems and handling challenges that have not been addressed well by existing learning theories and tools. This argument is verified by several case studies on coupling learning, including handling coupling in recommender systems, incorporating couplings into coupled clustering, coupling document clustering, coupled recommender algorithms and coupled behavior analysis for groups.
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