Multi-view TSK Fuzzy System via Collaborative Learning
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摘要: 传统模糊系统建模方法本质上是一种单视角学习模式，面向适合多视角处理的场景时，它们通常只能将每一视角割裂开来进行独立建模，这导致其所得系统泛化性能往往不令人满意。针对此缺陷，该文探讨具备多视角学习能力的模糊系统建模方法。为此，基于经典的L2型TSK模糊系统，通过引入具备多视角学习能力的协同学习项，该文提出了核心的多视角TSK型模糊系统(MV-TSK-FS)建模方法。MV-TSK-FS不仅能有效地利用各视角不同特征构成的独立样本信息，还能充分地利用各视角间由于相互关联而存在内在信息，以最终达到提高系统泛化性能的效果。在模拟数据集与真实数据集上的实验结果验证了较之于传统单视角模糊建模方法该多视角模糊系统有着更好的泛化性和适用性。Abstract: Conventional fuzzy system modeling methods essentially belong to the single-view learning modality. In multi-view-oriented data scenarios, they can only cope with each view separately, which is prone to incurring their unsatisfactory generalization performance. In response to such problem, the fuzzy system modeling method with the ability of multi-view learning is pursued. To this end, based on the classic L2 norm Takagi-Sugeno-Kang (TSK) fuzzy system, by means of the collaborative learning items qualified for multi-view learning, the core Multi-View TSK Fuzzy System (MV-TSK-FS) modeling method is presented. MV-TSK-FS can not only effectively utilize the independent components composed of the characteristics affiliated to each view, but also take full advantage of the potential information occurred by the interrelated effects among views, which eventually facilitates its relatively strong generalization ability. The experimental results performed on both synthetic and real-life datasets indicate that, compared with some traditional single-view methods, this propounded multi-view fuzzy modeling system owns preferable applicability as well as generalization.
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