Non-negative Matrix Factorization (NMF), a classical method for dimensionality reduction,
has been applied in many fields. It is based on the idea that negative numbers are physically …

In this article, we propose a semi-supervised non-negative matrix factorization (NMF) model
by means of elegantly modeling the label information. The proposed model is capable of …

Non-negative Tucker decomposition (NTD) is one of the most popular techniques for tensor
data representation. To enhance the representation ability of NTD by multiple intrinsic cues …

Non-negative matrix factorization (NMF) has recently attracted much attention due to its
good interpretation in perception science and widely applications in various fields. In this …

How to learn dimension-reduced representations of image data for clustering has been
attracting much attention. Motivated by that the clustering accuracy is affected by both the …

Feature selection is an interesting and challenging task in data analysis process. In this
paper, a novel algorithm named Regularized Matrix Factorization Feature Selection …

Label Distribution Learning (LDL) has attracted increasing research attentions due to its
potential to address the label ambiguity problem in machine learning and success in many …

Non-negative Tucker decomposition (NTD) has been developed as a crucial method for non-
negative tensor data representation. However, NTD is essentially an unsupervised method …

Label propagation is an important semi-supervised learning method that generalizes the
attributes of labelled samples to unlabelled samples based on the correlation of the data …

Nonnegative matrix factorization (NMF) algorithms are a series of dimensional reduction
techniques widely used in data preprocessing. To improve the performance of clustering …