Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a
set of base kernels. Constructing precise and local kernel matrices is proven to be of vital …

Anchor technology is popularly employed in multi-view subspace clustering (MVSC) to
reduce the complexity cost. However, due to the sampling operation being performed on …

-means method using Lloyd heuristic is a traditional clustering method which has played a
key role in multiple downstream tasks of machine learning because of its simplicity …

In this paper, we propose an enhanced deep clustering network (EDCN), which is
composed of a Feature Extractor, a Conditional Generator, a Discriminator and a Siamese …

Graph-based clustering has been considered as an effective kind of method in unsupervised
manner to partition various items into several groups, such as Spectral Clustering (SC) …

Unsupervised feature selection is an important topic in the fields of machine learning,
pattern recognition and data mining. The representation methods include adaptive-graph …

Unsupervised feature selection has gained considerable attention for extracting valuable
features from unlabeled datasets. Existing approaches typically rely on sparse mapping …

The number of nearest neighbors K and the utilized distance measure considerably impact
the performance of the K-nearest neighbor (K-NN) algorithm. The information provided by …

We consider the problem of spectral clustering under group fairness constraints, where
samples from each sensitive group are approximately proportionally represented in each …

Multi-view clustering has attracted extensive attention since it can integrate the
complementary information of different views. Nonetheless, most existing methods suffer …