Our experience of the world is multimodal-we see objects, hear sounds, feel texture, smell
odors, and taste flavors. Modality refers to the way in which something happens or is …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Supervised deep learning has been successfully applied for many recognition problems in
machine learning and computer vision. Although it can approximate a complex many-to-one …

Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

Multimodal Sentiment Analysis is an active area of research that leverages multimodal
signals for affective understanding of user-generated videos. The predominant approach …

In this paper, we study two challenging problems in incomplete multi-view clustering
analysis, namely, i) how to learn an informative and consistent representation among …

To define the cell populations that drive joint inflammation in rheumatoid arthritis (RA), we
applied single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA sequencing …

Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

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 …

Existing multi-view classification algorithms focus on promoting accuracy by exploiting
different views, typically integrating them into common representations for follow-up tasks …