Non-negative low rank and sparse graph for semi-supervised learning

By Liansheng Zhuang, Haoyuan Gao, Zhouchen Lin, Yi Ma, Xin Zhang, Nenghai Yu. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

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Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (2020, methodology): “Moreover, inspired by the motivation that learning a non-negative graph is beneficial to improve the clustering performance and make the learned graph more interpretable [36], we rewrite model (3) as follows to learn multiple non-negative graphs for multiview subspace learning:”
Ieee Transactions on Pattern Analysis and Machine Intelligence Laplacian Regularized Low-rank Representation and Its Applications (neutral): “In pattern recognition community, semisupervised learning has been attracting considerable attention over the past decades [46] [54] because the labeled samples are hard or expensive to acquire and whereas unlabeled ones are easy and inexpensive.”
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit (2015, result): “In particular, our result for deterministic arbitrary models is much stronger than the main result in [40].”