… for low-rank approximation of nonnegative matrix, providing a useful tool … semisupervised
NMF (SSNMF), where we jointly incorporate the data matrix and the (partial) class label matrix …

… Semi-supervised nonnegative matrix factorization exploits the strengths of matrix
factorization … enhance semi-supervised learning. In this paper, we design an effective Self-Supervised …

… [36] proposed a semisupervised NMF method called … Therefore, it is necessary to establish
a semi-supervised framework … limitations, a novel semisupervised NMF method is proposed in …

… Being aware of the importance of modeling the label information in semi-supervised NMF, …
take advantage of label information to guide the factorization. Specifically, the dissimilarity …

… the advantages of previous semi-supervised NMF methods, in this paper we propose a
novel robust semi-supervised NMF method, namely correntropy based semi-supervised NMF (…

… We propose a semi-supervised approach that uses markers to improve estimation. ► Application
… In this paper, we use the following Nonnegative Matrix Factorization (NMF) theoretical …

… : a semi-supervised non-negative matrix factorization frame… symmetric trifactorization of
the data similarity matrix to infer … a general framework for semi-supervised clustering. Existing …

… Recently semi-supervised non-negative matrix factorization (… However, the existing
semi-supervised NMF algorithms … In this paper, we propose a novel discriminative semi-supervised …

… nonnegative ma trix factorization method, called robust semi-supervised non negative matrix
factorization … Firstly, RSSNMF model imposes label information with a constraint matrix, so …

… -view clustering approach based on non-negative matrix factorization (NMF) [8], called
Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization (AMVNMF). The overall …