scholarly journals Ensemble non-negative matrix factorization methods for clustering protein–protein interactions

2008 ◽  
Vol 24 (15) ◽  
pp. 1722-1728 ◽  
Author(s):  
Derek Greene ◽  
Gerard Cagney ◽  
Nevan Krogan ◽  
Pádraig Cunningham
Keyword(s):  
Protein Interactions ◽  
Matrix Factorization ◽  
Protein Protein Interactions ◽  
Factorization Methods ◽  
Non Negative Matrix Factorization

scholarly journals Supervised non-negative matrix factorization methods for MALDI imaging applications

2018 ◽  
Vol 35 (11) ◽  
pp. 1940-1947 ◽  
Author(s):  
Johannes Leuschner ◽  
Maximilian Schmidt ◽  
Pascal Fernsel ◽  
Delf Lachmund ◽  
Tobias Boskamp ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Maldi Imaging ◽  
Factorization Methods ◽  
Non Negative Matrix Factorization

scholarly journals Robust semi-supervised non-negative matrix factorization for binary subspace learning

2021 ◽  
Author(s):  
Xiangguang Dai ◽  
Keke Zhang ◽  
Juntang Li ◽  
Jiang Xiong ◽  
Nian Zhang ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Original Data ◽  
Factorization Method ◽  
Subspace Learning ◽  
Image Clustering ◽  
The Face ◽  
Factorization Methods ◽  
Salt And Pepper ◽  
Weighted Constraint ◽  
Non Negative Matrix Factorization

AbstractNon-negative matrix factorization and its extensions were applied to various areas (i.e., dimensionality reduction, clustering, etc.). When the original data are corrupted by outliers and noise, most of non-negative matrix factorization methods cannot achieve robust factorization and learn a subspace with binary codes. This paper puts forward a robust semi-supervised non-negative matrix factorization method for binary subspace learning, called RSNMF, for image clustering. For better clustering performance on the dataset contaminated by outliers and noise, we propose a weighted constraint on the noise matrix and impose manifold learning into non-negative matrix factorization. Moreover, we utilize the discrete hashing learning method to constrain the learned subspace, which can achieve a binary subspace from the original data. Experimental results validate the robustness and effectiveness of RSNMF in binary subspace learning and image clustering on the face dataset corrupted by Salt and Pepper noise and Contiguous Occlusion.

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