  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.
