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Joint Multi-View Collaborative Clustering
To fill this gap, we propose the Joint Multi-View Collaborative Clustering (JMVCC) solution, which involves the generation of basic partitions using Non-negative Matrix Factorization (NMF) and the horizontal collaboration principle, followed by the fusion of these local partitions using ensemble clustering.
Multi-modal Multi-view Clustering based on Non-negative Matrix Factorization
By combining related objects, unsupervised machine learning techniques aim to reveal the underlying patterns in a data set.
A quantum learning approach based on Hidden Markov Models for failure scenarios generation
Therefore, in this paper, we will study and compare the results of HQMMs and classical Hidden Markov Models HMM on a real datasets generated from real small systems in the field of PSA.
Convex Non-negative Matrix Factorization Through Quantum Annealing
In the second step we use an alternative strategy between the two QUBO problems corresponding to W and G to find the global solution.
Co-clustering through Optimal Transport
The proposed method uses the entropy regularized optimal transport between empirical measures defined on data instances and features in order to obtain an estimated joint probability density function represented by the optimal coupling matrix.
