Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

Abstract Multi-View Clustering (MVC) is an emerging research area aiming to cluster
multiple views of the same data, which has recently drawn substantial attention. Various …

Multi-view clustering endeavors to effectively uncover consistent clustering patterns across
multiple data sources or feature spaces. This field grapples with two key challenges:(1) the …

With the continuous evolution of artificial intelligence and sensor technology, there is a
growing accumulation of unlabeled data. Uncovering valuable insights from this data has …

Current research on multi-view clustering (MVC) is pushing the boundaries of knowledge,
allowing the extraction of valuable insights from various points of view. Recently, many …

The graph-based multi-view clustering algorithms achieve decent clustering performance by
consensus graph learning of the first-order graphs from different views. However, the first …

Representation learning is an important topic in machine learning, pattern recognition, and
data mining research. Among many representation learning approaches, semi-nonnegative …

Clustering plays a crucial role in the field of data mining, where deep non-negative matrix
factorization (NMF) has attracted significant attention due to its effective data representation …

Semi-supervised nonnegative matrix factorization (NMF) methods have found extensive
utility in data clustering applications. However, these existing methods encounter challenges …

Dimensionality Reduction plays a pivotal role in improving feature learning accuracy and
reducing training time by eliminating redundant features, noise, and irrelevant data …