Non-linear dictionary learning with partially labeled data

While recent techniques for discriminative dictionary learning have demonstrated tremendous success in image analysis applications, their performance is often limited by the amount of labeled data available for training. Even though labeling images is difficult, it is relatively easy to collect unlabeled images either by querying the web or from public datasets. Using the kernel method, we propose a non-linear discriminative dictionary learning technique which utilizes both labeled and unlabeled data for learning dictionaries in the high-dimensional feature space. Furthermore, we show how this method can be extended for ambiguously labeled classification problem where each training sample has multiple labels and only one of them is correct. Extensive evaluation on existing datasets demonstrates that the proposed method performs significantly better than state of the art dictionary learning approaches when unlabeled images are available for training. HighlightsA dictionary learning method that utilizes labeled and unlabeled data is proposed.Using kernel trick, the proposed formulation is extended to the non-linear case.An efficient optimization procedure is proposed for solving this non-linear problem.Each training sample can have multiple labels and only one of them is correct.