1. A method for deriving a linear classifier comprising: (a) providing a training dataset comprising two subsets of data points, wherein one subset of data points is labeled in-class and the other subset of data points is labeled not-in-class; (b) providing an objective function, wherein said objective function comprises a 1-norm regularization term and a loss term, wherein said loss term comprises: i. a classification error for each data point labeled in the class and a weighting constant for the total in-class classification error; and ii. a classification error for each data point labeled not in the class and a weighting constant for the total not-in-class classification error; (c) minimizing said objective function for the training dataset; whereby said minimized objective function provides a linear classifier.