Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly
emerging efficient machine learning techniques which offer promising solutions for …

Semi-supervised learning utilizes labeled data and the geometric information in the
unlabeled data to construct a model whereas supervised learning makes use of the only …

Traditional partition-based clustering is very sensitive to the initialized centroids, which are
easily stuck in the local minimum due to their nonconvex objectives. To this end, convex …

Ramp-based twin support vector clustering (RampTWSVC) is a powerful clustering method,
which measures the within-cluster and between-cluster scatter by the bounded ramp …

SVM utilizes the hinge loss function and maximum margin to find the separating hyperplane.
In SVM, only the boundary instances/support vectors confine the separating hyperplane …

Lately, twin support vector machine and its variants have received extensive attention and in-
depth research in the field of large-scale pattern classification. However, they may lead to …

The existing multikernel graph clustering (MKGC) methods have emerged with notable
success on nonlinear clustering tasks since the graph learning can effectively capture graph …

Clustering is an unsupervised learning algorithm and it is widely used in machine learning.
Twin support vector clustering (TWSVC) is a new plane-based clustering algorithm, which …

K-means is a clustering method with an interpretable mechanism. However, its clustering
results are significantly affected by the location of the initial cluster centers. More importantly …

In the world of big data, extracting meaningful insights from large and continually growing
distributed datasets is a major challenge. Classical clustering algorithms are effective at …