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To achieve this, we design a new matrix factorization objective function and incorporate a label matrix factorization term as well as a network regularization term into it.
In our RNMF model, a label matrix factorization term and a network regularization term are incorporated into the NMF model for this purpose.
In RNMF, a label matrix factorization term and a network regularization term are incorporated into the NMF objective function to encode the network structure and label information.
In RNMF, a label matrix factorization term and a network regularization term are incorporated into the non-negative matrix factorization (NMF) objective function to seek a matrix factorization that respects the network structure and label information for classification prediction.
To achieve this, a label matrix factorization term and an additional network regularization term are incorporated into the NMF objective function, and an optimization scheme is developed to solve the objective function of the new NMF method.
In the proposed RNMF method, we need to set the the parameters α and β which quantify the importance of the label matrix factorization term and the network regularization term of the objective function in Eq. (11).
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Because of the methods used (adjacency matrix factorization and threshold condition), we call our model the factorization threshold model.
These factors are constrained to be non-negative, because one cannot have a negative contribution of a mutation signature to a tumor, and because a mutation signature cannot have a negative proportion of mutations of a given class; this is the origin of the term non-negative matrix factorization.
We demonstrate the effectiveness of our method via large-scale cross-validation experiments across two real datasets (MovieLens and Netflix) and show the superiority of our method over such state-of-the-art approaches as non-negative matrix factorization and singular value decomposition in terms of not only recommendation accuracy and diversity but also retrieval performance.
In terms of recommendation algorithm, matrix factorization leads to the shortest paths, followed by interpolation weights.
Then, we construct two diversified similarity neighborhood regularization terms and systemically integrate them into a matrix factorization model, which achieves the knowledge transfer of geographical neighborhoods in improving QoS prediction accuracy.
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