Semi-supervised dictionary learning with graph regularization and active points
This work addresses the need for better classification in databases with few labeled samples per class, though it appears incremental as it builds on existing semi-supervised dictionary learning approaches.
The paper tackles the problem of image classification with limited labeled data by proposing a semi-supervised dictionary learning method that uses graph regularization and active points, resulting in an improvement over state-of-the-art methods.
Supervised Dictionary Learning has gained much interest in the recent decade and has shown significant performance improvements in image classification. However, in general, supervised learning needs a large number of labelled samples per class to achieve an acceptable result. In order to deal with databases which have just a few labelled samples per class, semi-supervised learning, which also exploits unlabelled samples in training phase is used. Indeed, unlabelled samples can help to regularize the learning model, yielding an improvement of classification accuracy. In this paper, we propose a new semi-supervised dictionary learning method based on two pillars: on one hand, we enforce manifold structure preservation from the original data into sparse code space using Locally Linear Embedding, which can be considered a regularization of sparse code; on the other hand, we train a semi-supervised classifier in sparse code space. We show that our approach provides an improvement over state-of-the-art semi-supervised dictionary learning methods.