The modern science of networks has made significant advancement in the modeling of
complex real-world systems. One of the most important features in these networks is the …

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 …

Community structure is ubiquitous in real-world complex networks. The task of community
detection over these networks is of paramount importance in a variety of applications …

Semi-supervised image classification aims to classify a large quantity of unlabeled images
by typically harnessing scarce labeled images. Existing semi-supervised methods often …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

Multi-view clustering is widely used to improve clustering performance. Recently, the
subspace clustering tensor learning method based on Markov chain is a crucial branch of …

Constrained low-rank matrix approximations have been known for decades as powerful
linear dimensionality reduction techniques able to extract the information contained in large …

Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and
it has been successfully applied to data mining and machine learning community, due to its …

Nonnegative matrix factorization (NMF) has become a very popular technique in machine
learning because it automatically extracts meaningful features through a sparse and part …

In this paper, we design a fast hierarchical clustering algorithm for high-resolution
hyperspectral images (HSI). At the core of the algorithm, a new rank-two nonnegative matrix …