NMFk couples an NMF procedure with a custom semi-supervised clustering procedure. Since NMF requires a priori knowledge of the number of sources (denoted herein�...
A semi-supervised clustering procedure is applied to trial results, and the clustering results are evaluated for robustness using measures for reconstruction�...
Sources identification using shifted non-negative matrix factorization combined with semi-supervised clustering � Computer Science, Engineering. ArXiv � 2016.
In the paper, we propose a deep nonsmooth nonnegative matrix factorization (nsNMF) network with semi-supervised learning for synthetic aperture radar (SAR)�...
Missing: identification shifted
Mar 8, 2018 � The above analyses demonstrate the applicability of our new protocol for identification of unknown delayed sources based on Shifted Nonnegative�...
... Source identification by non-negative matrix factorization combined with semi-supervised clus- tering (2018). US Patent App. 15/690,176. Page 28. 28. Gopinath�...
Apr 2, 2024 � This paper proposes a new approach for modularity-based community detection which is similar to symmetric NMF.
Missing: shifted | Show results with:shifted
NMFk is a novel unsupervised machine learning methodology which allows for automatic identification of the optimal number of features (signals) present in�...
In this paper, we propose a novel method, namely Guided Semi-Supervised Non-negative Matrix Factorization (GSSNMF), that performs both classification and topic�...
Missing: Sources shifted
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Sources identification using shifted non-negative matrix factorization combined with semi-supervised clustering. Article. Full-text available. Dec 2016.