This survey aims at providing a state-of-the-art overview of feature selection and fusion
strategies, which select and combine multi-view features effectively to accomplish …

Multi-view feature selection aims at obtaining a subset of informative features from
heterogeneous feature domains. Recent graph based approaches mostly learn view …

Weighted multi-view clustering (MVC) aims to combine the complementary information of
multi-view data (such as image data with different types of features) in a weighted manner to …

Brain disorders, such as Autism Spectrum Disorder (ASD), alter brain functional (from fMRI)
and structural (from diffusion MRI) connectivities at multiple levels and in varying degrees …

Different kinds of features describe different aspects of image data, and each feature can be
treated as a view when we take it as a particular understanding of images. Leveraging …

Multimedia content analysis and understanding, such as action recognition and image
classification, is a fundamental research problem. One effective strategy to improve the …

various machine learning applications frequently incorporate multi-view data, which is
characterized by distinct features representing different points of view or aspects. However …

The number of dimensions (also called features) of the data has increased significantly in
various real applications such as healthcare, social media and online learning [2]. Figure 1.1 …