A Study on Non-sparse Dictionary Learning for Pattern Classification

Dictionary learning (DL) approach has been successfully applied to many pattern classification problems. Sparse property has played an important role in the success of DL-based classification models. However, the sparsity constraints make the learning problem expensive. Recently, there has been an emerged trend in relaxing the sparsity constraints by using L2-norm constraint. The new approach has shown its advantages in both accuracy and classification time. However, the relationship between the quality of the data and the dictionary learning issues that affect the performance of the system has not been investigated. In this paper, we present a comparative study on non-sparse coding dictionary learning for pattern classification. We then propose a dictionary learning model with a non-sparsity constraint on representation coefficients using L2-norm. Our experimental results on three popular benchmark datasets for image classification show that our proposed model can outperform state-of-the-art models and be a promising approach for dictionary learning based classification.