Patient similarity networks (PSNs), where patients are represented as nodes and their
similarities as weighted edges, are being increasingly used in clinical research. These …

In the era of big data, where vast amounts of information are being generated and collected
at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal …

Multimodal sentiment analysis (MSA) aims to infer emotions from linguistic, auditory, and
visual sequences. Multimodal information representation method and fusion technology are …

Advances in imaging acquisition techniques allow multiple imaging modalities to be
collected from the same subject. Each individual modality offers limited yet unique views of …

Neuroimaging data typically include multiple modalities, such as structural or functional
magnetic resonance imaging, diffusion tensor imaging, and positron emission tomography …

This paper overviews an appealing unsupervised learning method named independent
vector analysis (IVA) for its promising applications, such as in audio/speech signal …

This work proposes a novel generative multimodal approach to jointly analyze multimodal
data while linking the multimodal information to colors. We apply our proposed framework …

With the increasing availability of large‐scale multimodal neuroimaging datasets, it is
necessary to develop data fusion methods which can extract cross‐modal features. A …

Objective: Brain function is understood to be regulated by complex spatiotemporal
dynamics, and can be characterized by a combination of observed brain response patterns …

A key challenge in neuroscience is to understand the structural and functional relationships
of the brain from high-dimensional, multimodal neuroimaging data. While conventional …