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Matrix factorization and interpolation weights lead to the shortest distances.
Then Bayesian learning methods for matrix factorization and other related tasks are introduced.
We have presented a novel approach to solve the EEG inverse problem, which is based on matrix factorization and regularization.
Because of the methods used (adjacency matrix factorization and threshold condition), we call our model the factorization threshold model.
This probabilistic framework is called "Bayesian non-negative matrix factorization" and was later used in experiments for automatic transcription of polyphonic music [15].
In our work, we suggest a new model for scale-free networks generation, which is based on the matrix factorization and has a geographical interpretation.
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For the SO problem, we have proposed an MTY-PC infeasible-IPM, which requires two matrix factorizations and at most three back-solves for each iteration.
Table 1 lists the number of eigenvalues within these triangles, the number of used shifts, that is, used matrix factorizations, and the number of overall Arnoldi iterations, that is, matrix-block products.
Since the MTY-PC algorithm requires two matrix factorizations and at most three back-solves for each iteration, it is generally divided into two steps, which are the predictor step and the corrector step.
Sparsity penalization has recently attracted intense interest in regression analysis (Friedman, 2008), variable selection (Chun and Keles, 2010; Lê Cao et al., 2008), matrix factorization (Kim and Park, 2007; Shen and Huang, 2008) and module discovery.
SimBoost takes informative features from the drug and target similarities and from a matrix factorization model, and trains a gradient boosting tree model.
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