Sentence examples for matrix factorization approaches from inspiring English sources

Listed in Table 4 below are some examples of the scientific applications and the potential constraints of matrix factorization approaches.

Finally, we presented detailed examples of the use of constrained matrix factorization approaches on different spectroscopy data, including X-ray microscopy and scanning probe microscopy datasets.

The application of the matrix factorization approaches have been mostly limited to decomposing a single time-frequency matrix into significant time and frequency components to reduce the dimensionality and extract features for consequent classification [34, 39].

Further, the underlying Boolean matrix factorization approach allows the user to identify multiple overlapping clusters of trans-factors that jointly account for as many interactions as possible, casting the problem into an approximate interaction coverage task.

In[19], the authors present a probabilistic matrix factorization approach for the recommender system.

Knowledge was transferred between text and images using matrix factorization approach by Zhu et al. [46].

Finally, we present a nonnegative matrix factorization approach to learn the model parameters.

From these results, we can see that the proposed Matrix Factorization approach outperforms both the Group Lasso and Trace Norm regularizers.

Thus, inspired by the matrix factorization approach we achieved the following model behavior: the edges in our model are assumed to be formed when a pair of nodes is spatially close and/or has large weights.

All of this implies that the Trace Norm tends to induce low rank dense solutions, which are not biologically plausible.According to Figures 3 and 4, the position of the MAS obtained from the BES matrix estimated by the Matrix Factorization approach, the Group Lasso, and Trace Norm regularizers follows closely the position of the true MAS.

We have used cross-validation to select the regularization parameter λ associated to the Group Lasso and Trace Norm regularizers, as well as the parameters λ and K in the case of the Matrix Factorization approach (K∈ [ 1,2,3,…,10], λ∈ [ 10−3,10−2,10−1,…,103]): the rows of Y are randomly partitioned into three groups of approximately equal size.