Abstract The Hilbert–Schmidt independence criterion (HSIC) was originally designed to
measure the statistical dependence of the distribution-based Hilbert space embedding in …

Although demonstrating great success, previous multi-view unsupervised feature selection
(MV-UFS) methods often construct a view-specific similarity graph and characterize the local …

Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a
consensus representation from different views but ignore the important information hidden in …

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 …

The main purpose of multi-view subspace clustering is to reveal the intrinsic low-
dimensional architecture of data points according to their multi-view characteristics …

Multi-modal classification (MMC) aims to integrate the complementary information from
different modalities to improve classification performance. Existing MMC methods can be …

Graph-based multi-view clustering, with its ability to mine potential associations between
data samples, has attracted extensive attention. However, existing methods directly learn …

As data collected from different sources have multiple representations, multi-view learning
has become an important paradigm of machine learning. To exploit multi-view data …

Multi-view learning becomes increasingly attractive and promising because multimodal or
multi-view data are commonly encountered in real-world applications. In this study, we …

The bimodal ultrasound, namely B-mode ultrasound (BUS) and elastography ultrasound
(EUS), provide complementary information to improve the diagnostic accuracy of breast …