Confident Identification of Relevant Objects Based on Nonlinear Rescaling Method and Transductive Inference

We present a novel machine learning algorithm to identify relevant objects from a large amount of data. This approach is driven by linear discrimination based on nonlinear rescaling (NR) method and transductive inference. The NR algorithm for linear discrimination (NRLD) computes both the primal and the dual approximation at each step. The dual variables associated with the given labeled data-set provide important information about the objects in the data-set and play the key role in ordering these objects. A confidence score based on a transductive inference procedure using NRLD is used to rank and identify the relevant objects from a pool of unlabeled data. Experimental results on an unbalanced protein data-set for the drug target prioritization and identification problem are used to illustrate the feasibility of the proposed identification algorithm.