Structure-Aware Classification using Supervised Dictionary Learning
This work addresses classification tasks by enhancing discriminative power in sparse representations, but it appears incremental as it builds on existing dictionary learning methods with added regularization.
The authors tackled the problem of improving classification accuracy by developing a supervised dictionary learning algorithm that preserves local geometry in both data and feature dimensions using graph-based regularization. The method demonstrated better performance compared to other dictionary-based approaches on various single-label and multi-label datasets.
In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data. A graph-based regularization explicitly takes into account the local manifold structure of the data points. A second graph regularization gives similar treatment to the feature domain and helps in learning a more robust dictionary. Both graphs can be constructed from the training data or learned and adapted along the dictionary learning process. The combination of these two terms promotes the discriminative power of the learned sparse representations and leads to improved classification accuracy. The proposed method was evaluated on several different datasets, representing both single-label and multi-label classification problems, and demonstrated better performance compared with other dictionary based approaches.