As the observed examples are stacked as rows (or columns) in a data matrix, either some of the entries may be affected by (1) additive outliers (positive or negative values) or missing data, or (2) entire rows (
Such a task can be solved by using a matrix completion model of the form: (28)minX‖PΩ(X−M)‖F2+λ‖X‖r, where X denotes the complete rating matrix. This task is a significant metric for evaluating the performance of matrix completion models and has attached much attention in ...
B.2.1 Vector-like quarks We list in Table 4 some examples of heavy BSM states, such as vector-like quarks, that can generate Higgs-quark coef- Fixing the semileptonic operator to fit R ν K would therefore fix also the coefficient responsible for Bs mixing. Hence, we have to include ...
We solved the DTI problem by using neural networks with a strong ability to capture non-linearity from raw data and learn deep features from a ranking learning perspective; (2) To better predict DTIs, especially for new drugs and targets, we added drug–drug and target–target similarities ...
Finally, we also examine the Matthews correlation coefficient, which is a summary statistic that incorporates information about the entire confusion matrix. See [22] for more information about these statistics. In all of the tests that we perform in this work, the precision and Matthews correlation...
we compare the imputed metabolite ranks to the true ranks of the features that were simulated as missing. The chosen performance metric is Spearman’s rank correlation coefficient (\rho), computed between actual and predicted metabolite ranks. Correlation coefficients are computed separately for each ...
the latter least-squares problem can be solved bydirectmethods, such as a QR-factorization combined with a linear system solver. Such methods have a uniform computational cost regardless of the condition number of the problem. By contrast, since no (provably) numerically stable direct algorithms fo...
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Fig. 6. Examples of reconstructed frames from Drop dataset. Download: Download high-res image (597KB) Download: Download full-size image Fig. 7. Comparing results from all models used in the paper on Kobe and Drop datasets, where (a) and (c) represent frame-wise PSNR, (b) and (d) ...
The C-step is solved by singular value thresholding. As in practice the desired rank is usually much smaller than both p and q, the computation cost of performing SVD can be well controlled. When updating the unknown dispersion parameters, the problem is separable in each ϕk, and can be...