Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

The era of artificial neural network (ANN) began with a simplified application in many fields
and remarkable success in pattern recognition (PR) even in manufacturing industries …

Conventional multiview clustering seeks to partition data into respective groups based on
the assumption that all views are fully observed. However, in practical applications, such as …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

With the increasing importance and complexity of data pipelines, data quality became one of
the key challenges in modern software applications. The importance of data quality has …

Although multi-view learning has made significant progress over the past few decades, it is
still challenging due to the difficulty in modeling complex correlations among different views …

With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …

Abstract Machine learning (ML) has been increasingly used within cardiology, particularly in
the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML …

Psychiatry today must gain a better understanding of the common and distinct
pathophysiological mechanisms underlying psychiatric disorders in order to deliver more …

Nowadays, with the rapid development of data collection sources and feature extraction
methods, multi-view data are getting easy to obtain and have received increasing research …