  Data collected by multiple methods or from multiple sources is called
multi-view data. To make full use of the multi-view data, multi-view learning
plays an increasingly important role. Traditional multi-view learning methods
rely on a large number of labeled and completed multi-view data. However, it is
expensive and time-consuming to obtain a large number of labeled multi-view
data in real-world applications. Moreover, multi-view data is often incomplete
because of data collection failures, self-deficiency, or other reasons.
Therefore, we may have to face the problem of fewer labeled and incomplete
multi-view data in real application scenarios. In this paper, a transductive
semi-supervised incomplete multi-view TSK fuzzy system modeling method
(SSIMV_TSK) is proposed to address these challenges. First, in order to
alleviate the dependency on labeled data and keep the model interpretable, the
proposed method integrates missing view imputation, pseudo label learning of
unlabeled data, and fuzzy system modeling into a single process to yield a
model with interpretable fuzzy rules. Then, two new mechanisms, i.e. the
bidirectional structural preservation of instance and label, as well as the
adaptive multiple alignment collaborative learning, are proposed to improve the
robustness of the model. The proposed method has the following distinctive
characteristics: 1) it can deal with the incomplete and few labeled multi-view
data simultaneously; 2) it integrates the missing view imputation and model
learning as a single process, which is more efficient than the traditional
two-step strategy; 3) attributed to the interpretable fuzzy inference rules,
this method is more interpretable. Experimental results on real datasets show
that the proposed method significantly outperforms the state-of-the-art
methods.
