In this paper, we propose a novel semi-supervised non-negative matrix factorization (SEMINMF) algorithm, which not only utilizes the local ...
In this paper, we propose a novel semi-supervised non-negative matrix factorization (SEMINMF) algorithm, which not only utilizes the local structure of the data ...
Abstract: Non-negative matrix factorization (NMF) plays an important role in multivariate data analysis, and has been widely applied in information ...
This paper proposes a novel semi-supervised non-negative matrix factorization (SEMINMF) algorithm, which not only utilizes the local structure of the data ...
Semi-supervised non-negative matrix factorization for image clustering with graph Laplacian. Yangcheng He, Hongtao Lu, Saining Xie. Computer Science. Research ...
Extensive experimental results show the effectiveness and robustness of CSNMF in image clustering tasks, compared with several state-of-the-art methods.
This work presents semi-supervised NMF (SSNMF), where they jointly incorporate the data matrix and the (partial) class label matrix into NMF, and develops ...
May 11, 2020 · Bibliographic details on Semi-supervised non-negative matrix factorization for image clustering with graph Laplacian.
In this paper, we design an effective Self-Supervised Semi-Supervised Nonnegative Matrix Factorization (S 4 NMF) in a semi-supervised clustering setting.
This paper is concerned with the design of a non-negative matrix factorization algorithm for image analysis. This can be used in the context of blind source�...