Our method then nonlinearly maps the embedding features of complete data into a high-dimensional space to discover linear separability.

First, the proposed method builds a deep embedding feature learning and clustering model for each view individually.

To avoid these issues, this work presents an imputation-free and fusion-free deep IMVC framework. Previous IMVC methods suffer from the following issues: (1) the inaccurate imputation or padding for missing data negatively affects the clustering performance, (2) the quality of features after fusion might be interfered by the low-quality views, especially the inaccurate imputed views. Incomplete multi-view clustering (IMVC) is an important unsupervised approach to group the multi-view data containing missing data in some views. Machine Learning (ML), Data Mining & Knowledge Management (DMKM) Abstract Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518000, China School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

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