Rapid technological advances have led to the production of different types of biological data
and enabled construction of complex networks with various types of interactions between …

Attributed graph clustering is a prominent research area, catering to the increasing need for
understanding real-world systems by uncovering exhaustive meaningful latent knowledge …

Gene expression data have become increasingly important in machine learning and
computational biology over the past few years. In the field of gene expression analysis …

Community detection has become an important research topic in machine learning due to
the proliferation of network data. However, most existing methods have been developed …

Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of
high-dimensional data as it automatically extracts sparse and meaningful features from a set …

Abstract In recent times, Symmetric Nonnegative Matrix Factorization (SNMF), a derivative of
Nonnegative Matrix Factorization (NMF), has surfaced as a promising technique for graph …

Nonnegative Matrix Factorization (NMF) has been one of the most widely used clustering
techniques for exploratory data analysis. However, since each data point enters the …

Subspace distance is an invaluable tool exploited in a wide range of feature selection
methods. The power of subspace distance is that it can identify a representative subspace …

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 …

For most problems in science and engineering we can obtain data sets that describe the
observed system from various perspectives and record the behavior of its individual …