A novel evaluation criteria to estimate label

Abstract

In supervised learning, annotation is hard work because training data must be labeled and a lot of training data is needed. Therefore, we propose a novel label estimation method based on Fisher criterion to estimate label of unlabeled data from a small amount of labeled data. Fisher criterion maximizes between-class variance and minimizes within-class variance. Since the Fisher criterion is only used for linearly separable data, we apply our proposed method to linearly inseparable data. We demonstrate that the proposed method is effective in estimating the labels of linearly inseparable data.