Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …

Graph-based subspace clustering methods have exhibited promising performance.
However, they still suffer some of these drawbacks: they encounter the expensive time …

In recent years, symmetric non-negative matrix factorization (SNMF), a variant of non-
negative matrix factorization (NMF), has emerged as a promising tool for data analysis. This …

Subspace clustering methods based on data self-expression have become very popular for
learning from data that lie in a union of low-dimensional linear subspaces. However, the …

Multiple kernel learning (MKL) is generally recognized to perform better than single kernel
learning (SKL) in handling nonlinear clustering problem, largely thanks to MKL avoids …

Most popular clustering methods map raw image data into a projection space in which the
clustering assignment is obtained with the vanilla k-means approach. In this article, we …

Multiview subspace clustering has received significant attention as the availability of diverse
of multidomain and multiview real-world data has rapidly increased in the recent years …

Semi-supervised nonnegative matrix factorization exploits the strengths of matrix
factorization in successfully learning part-based representation and is also able to achieve …

In this article, we propose a deep extension of sparse subspace clustering, termed deep
subspace clustering with L1-norm (DSC-L1). Regularized by the unit sphere distribution …

Samples are commonly represented as sparse vectors in many dictionary representation
algorithms. However, this method may result in loss of discriminatory information. Moreover …