A Brief Summary of Dictionary Learning Based Approach for Classification (revised)
It provides a taxonomy for researchers working on dictionary learning in classification, but is incremental as it summarizes existing methods.
This paper reviews dictionary learning methods for classification, categorizing them into approaches that make dictionaries discriminative or force sparse coefficients to be discriminative.
This note presents some representative methods which are based on dictionary learning (DL) for classification. We do not review the sophisticated methods or frameworks that involve DL for classification, such as online DL and spatial pyramid matching (SPM), but rather, we concentrate on the direct DL-based classification methods. Here, the "so-called direct DL-based method" is the approach directly deals with DL framework by adding some meaningful penalty terms. By listing some representative methods, we can roughly divide them into two categories, i.e. (1) directly making the dictionary discriminative and (2) forcing the sparse coefficients discriminative to push the discrimination power of the dictionary. From this taxonomy, we can expect some extensions of them as future researches.