In the past decade, multi-view clustering has received a lot of attention due to popularity data. However, not all samples can be observed from every view some unavoidable factors, resulting in incomplete (IMC) problem. Up until now, most efforts for IMC problem have been made on learning consensus representations or graphs, while many missing views are ignored, making it impossible capture inf...

Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restricting their feasibility for large-scale datasets. Second, typically fuse information via one-stage fusion, neglecting possibilities in multi-stage fusions. Third, dataset-specific hyperparameter-tuning is frequently req...

This paper is a reflection upon a common practice of solving various types of learning problems by optimizing arbitrarily chosen criteria in the hope that they are well correlated with the criterion actually used for assessment of the results. This issue has been investigated using clustering as an example, hence a unified view of clustering as an optimization problem is first proposed, stemmin...