Incremental Learning and Decremented Characterization of Gene Expression Data Analysis

In this study, we present incremental learning and decremented characterization of regularized generalized eigenvalue classification (ILDC-ReGEC), a novel algorithm to train a generalized eigenvalue classifier with a substantially smaller subset of points and features of the original data. The proposed method provides a constructive way to understand the influence of new training data on an existing classification model and the grouping of features that determine the class of samples. The proposed algorithm is compared with other well known solutions. Experimental results are conducted on publicly available datasets and standard parameters are used for evaluation.