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Found 4 papers, 0 papers with code
Neyman-Pearson Classification under High-Dimensional Settings
Most existing binary classification methods target on the optimization of the overall classification risk and may fail to serve some real-world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other.
Multivariate Regression with Calibration
We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models.
Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification
For the first approach, we note that the zero vector can be taken as the initial basic (infeasible) solution for the linear programming problem and therefore, if the true signal is very sparse, some variants of the simplex method can be expected to take only a small number of pivots to arrive at a solution.
Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery
We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models.
