Multi-view learning (MVL) has attracted increasing attention and achieved great practical
success by exploiting complementary information of multiple features or modalities …

This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

The success of existing multi-view clustering methods heavily relies on the assumption of
view consistency and instance completeness, referred to as the complete information …

The world provides us with data of multiple modalities. Intuitively, models fusing data from
different modalities outperform their uni-modal counterparts, since more information is …

Despite the remarkable success of deep multi-modal learning in practice, it has not been
well-explained in theory. Recently, it has been observed that the best uni-modal network …

Subspace clustering is an effective method that has been successfully applied to many
applications. Here, we propose a novel subspace clustering model for multi-view data using …

In real-world applications, it is common that only a portion of data is aligned across views
due to spatial, temporal, or spatiotemporal asynchronism, thus leading to the so-called …

We abstract the features (ie learned representations) of multi-modal data into 1) uni-modal
features, which can be learned from uni-modal training, and 2) paired features, which can …

Multi-view clustering (MVC) aims to fuse the information among multiple views to achieve
effective clustering. Many MVC algorithms based on semi-nonnegative matrix factorization …

Most existing approaches address multi-view subspace clustering problem by constructing
the affinity matrix on each view separately and afterwards propose how to extend spectral …