The development of information gathering and extraction technology has led to the
popularity of multi-view data, which enables samples to be seen from numerous …

Despite significant progress, there remain three limitations to the previous multi-view
clustering algorithms. First, they often suffer from high computational complexity, restricting …

Multi-view semi-supervised classification is a typical task to classify data using a small
amount of supervised information, which has attracted a lot of attention from researchers in …

Enhancing the accuracy of dense classification with limited labeled data and abundant
unlabeled data, known as semi-supervised semantic segmentation, is an essential task in …

Multi-view (or-modality) representation learning aims to understand the relationships
between different view representations. Existing methods disentangle multi-view …

Multi-view representation learning aims to extract comprehensive information from multiple
sources. It has achieved significant success in applications such as video understanding …

Semi-supervised multi-view classification is a critical research topic that leverages the
discrepancy between different views and limited annotated samples for pattern recognition …

Recent semi-supervised learning (SSL) algorithms such as FixMatch achieve state-of-the-art
performance by exploiting consistency regularization and entropy minimization techniques …

Palmprint recognition has been widely applied to security authentication due to its rich
characteristics, ie, local direction, wrinkle, and texture. However, different types of palmprint …

Multi-view data describes an image sample with different modalities of features, thus
provides a more comprehensive description of data. Its three basic characteristics, ie …