Matrix factorization is an important technology that obtains the latent representation of data
by mining the potential structure of data. As two popular matrix factorization techniques …

The quality of obtained features by representation learning determines the performance of a
learning algorithm and subsequent application tasks (eg, high-dimensional data clustering) …

The concept factorization algorithm has received widespread attention and achieved
remarkable results in the field of clustering. However, when modeling this clustering …

Learning an informative data representation is of vital importance in multidisciplinary
applications, eg, face analysis, document clustering and collaborative filtering. As a very …

Abstract Concept Factorization (CF), as a novel paradigm of representation learning, has
demonstrated superior performance in multi-view clustering tasks. It overcomes limitations …

Previous studies have demonstrated that matrix factorization techniques, such as
Nonnegative Matrix Factorization (NMF) and Concept Factorization (CF), have yielded …

Matrix factorization methods have been widely applied for data representation. Traditional
concept factorization, however, fails to utilize the discriminative structure information and the …

We propose a novel multi-view document clustering method with the graph-regularized
concept factorization (MVCF). MVCF makes full use of multi-view features for more …

Abstract Nonnegative Matrix Factorization (NMF) and Concept Factorization (CF) have been
widely used for different purposes such as feature learning, dimensionality reduction and …

Major depressive disorder (MDD) is having an increasingly severe impact worldwide, which
creates a pressing need for an efficient and objective method of depression detection …