Adaptively Unified Semi-Supervised Dictionary Learning with Active Points
Semi-supervised dictionary learning aims to construct a dictionary by utilizing both labeled and unlabeled data. To enhance the discriminative capability of the learned dictionary, numerous discriminative terms have been proposed by evaluating either the prediction loss or the class separation criterion on the coding vectors of labeled data, but with rare consideration of the power of the coding vectors corresponding to unlabeled data. In this paper, we present a novel semi-supervised dictionary learning method, which uses the informative coding vectors of both labeled and unlabeled data, and adaptively emphasizes the high confidence coding vectors of unlabeled data to enhance the dictionary discriminative capability simultaneously. By doing so, we integrate the discrimination of dictionary, the induction of classifier to new testing data and the transduction of labels to unlabeled data into a unified framework. To solve the proposed problem, an effective iterative algorithm is designed. Experimental results on a series of benchmark databases show that our method outperforms other state-of-the-art dictionary learning methods in most cases.