Active Dictionary Learning in Sparse Representation Based Classification
This work addresses dictionary optimization for sparse representation in classification tasks, offering a domain-specific improvement over existing methods.
The paper tackled the problem of constructing effective dictionaries for sparse representation-based classification by introducing an active dictionary learning method that selects atoms based on classification and reconstruction errors, achieving improved performance on UCI and face datasets.
Sparse representation, which uses dictionary atoms to reconstruct input vectors, has been studied intensively in recent years. A proper dictionary is a key for the success of sparse representation. In this paper, an active dictionary learning (ADL) method is introduced, in which classification error and reconstruction error are considered as the active learning criteria in selection of the atoms for dictionary construction. The learned dictionaries are caculated in sparse representation based classification (SRC). The classification accuracy and reconstruction error are used to evaluate the proposed dictionary learning method. The performance of the proposed dictionary learning method is compared with other methods, including unsupervised dictionary learning and whole-training-data dictionary. The experimental results based on the UCI data sets and face data set demonstrate the efficiency of the proposed method.