Coupling learning of complex interactions

Longbing Cao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

117 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)167-186
Number of pages20
JournalInformation Processing and Management
Volume51
Issue number2
DOIs
Publication statusPublished - Mar 2015
Externally publishedYes

Keywords

  • Coupling learning
  • Coupling relationship
  • Interaction learning
  • Non-IIDness learning
  • Invisibility learning

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