Kernelized Supervised Dictionary Learning
This work addresses the need for more effective supervised dictionary learning in machine learning, though it appears incremental as it builds on existing dependency maximization techniques.
The paper tackles the problem of supervised dictionary learning by incorporating class labels to maximize dependency between signals and labels using the Hilbert Schmidt independence criterion, resulting in a compact and fast approach that outperforms existing methods on real-world data.
In this paper, we propose supervised dictionary learning (SDL) by incorporating information on class labels into the learning of the dictionary. To this end, we propose to learn the dictionary in a space where the dependency between the signals and their corresponding labels is maximized. To maximize this dependency, the recently introduced Hilbert Schmidt independence criterion (HSIC) is used. One of the main advantages of this novel approach for SDL is that it can be easily kernelized by incorporating a kernel, particularly a data-derived kernel such as normalized compression distance, into the formulation. The learned dictionary is compact and the proposed approach is fast. We show that it outperforms other unsupervised and supervised dictionary learning approaches in the literature, using real-world data.