Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

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

It is a challenging research topic to identify plant disease based on diseased leaf image
processing techniques due to the complexity of the diseased leaf images. Deep learning …

Learning graphs from data automatically have shown encouraging performance on
clustering and semisupervised learning tasks. However, real data are often corrupted, which …

Multiple kernel clustering (MKC) algorithms optimally combine a group of pre-specified base
kernel matrices to improve clustering performance. However, existing MKC algorithms …

View missing and label missing are two challenging problems in the applications of multi-
view multi-label classification scenery. In the past years, many efforts have been made to …

Feature selection (FS) is an important component of many pattern recognition tasks. In these
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …

Graph convolutional network (GCN) outputs powerful representation by considering the
structure information of the data to conduct representation learning, but its robustness is …

Multiple kernel learning (MKL) is generally recognized to perform better than single kernel
learning (SKL) in handling nonlinear clustering problem, largely thanks to MKL avoids …

BACKGROUND: Breast cancer is one of the most common cancers with a high mortality rate
among women. With the early diagnosis of breast cancer survival will increase from 56% to …