ADL: Active dictionary learning for sparse representation
Date of Original Version
Using dictionary atoms to reconstruct input vectors is of great interest in spare representation. However, a key challenge is how to find a proper dictionary. In this paper, we introduce an active dictionary learning (ADL) method which incorporates active learning criteria to select atoms for dictionary construction with the consideration of both classification and reconstruction errors. Specifically, we apply a sparse representation based classification (SRC) method to calculate the learned dictionary and use the classification accuracy and the reconstruction error to evaluate the proposed dictionary learning method. In our experiments, we compare the performance of our proposed dictionary learning method with many other methods, including unsupervised dictionary learning and whole-training-data dictionary, on several UCI data sets and the Extended Yale B face data set. The superior performance demonstrates the effectiveness of the proposed method.
Publication Title, e.g., Journal
Proceedings of the International Joint Conference on Neural Networks
Tang, Bo, Jin Xu, Haibo He, and Hong Man. "ADL: Active dictionary learning for sparse representation." Proceedings of the International Joint Conference on Neural Networks 2017-May, (2017): 2723-2729. doi: 10.1109/IJCNN.2017.7966191.