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Data collected by multiple methods or from multiple sources is calledmulti-view data. To make full use of the multi-view data, multi-view learningplays an increasingly important role. Traditional multi-view learning methodsrely on a large number of labeled and completed multi-view data. However, it isexpensive and time-consuming to obtain a large number of labeled multi-viewdata in real-world applications. Moreover, multi-view data is often incompletebecause of data collection failures, self-deficiency, or other reasons.Therefore, we may have to face the problem of fewer labeled and incompletemulti-view data in real application scenarios. In this paper, a transductivesemi-supervised incomplete multi-view TSK fuzzy system modeling method(SSIMV_TSK) is proposed to address these challenges. First, in order toalleviate the dependency on labeled data and keep the model interpretable, theproposed method integrates missing view imputation, pseudo label learning ofunlabeled data, and fuzzy system modeling into a single process to yield amodel with interpretable fuzzy rules. Then, two new mechanisms, i.e. thebidirectional structural preservation of instance and label, as well as theadaptive multiple alignment collaborative learning, are proposed to improve therobustness of the model. The proposed method has the following distinctivecharacteristics: 1) it can deal with the incomplete and few labeled multi-viewdata simultaneously; 2) it integrates the missing view imputation and modellearning as a single process, which is more efficient than the traditionaltwo-step strategy; 3) attributed to the interpretable fuzzy inference rules,this method is more interpretable. Experimental results on real datasets showthat the proposed method significantly outperforms the state-of-the-artmethods.

TSK Fuzzy System Towards Few Labeled Incomplete Multi-View Data Classification