Support vector machine active learning by Hessian regularization

It is time-consuming and expensive to gather and label the growing multimedia data that is easily accessible with the prodigious development of Internet technology and digital sensors. Hence, it is essential to develop a technique that can efficiently be utilized for the large-scale multimedia data especially when labeled data is rare. Active learning is showing to be one useful approach that greedily chooses queries from unlabeled data to be labeled for further learning and then minimizes the estimated expected learning error. However, most active learning methods only take into account the labeled data in the training of the classifier. In this paper, we introduce a semi-supervised algorithm to learn the classifier and then perform active learning scheme on top of the semi-supervised scheme. Particularly, we employ Hessian regularization into support vector machine to boost the classifier. Hessian regularization exploits the potential geometry structure of data space (including labeled and unlabeled data) and then significantly leverages the performance in each round. To evaluate the proposed algorithm, we carefully conduct extensive experiments including image segmentation and human activity recognition on popular datasets respectively. The experimental results demonstrate that our method can achieve a better performance than the traditional active learning methods.