Unidirectional Representation-Based Efficient Dictionary Learning

Dictionary learning (DL) has been widely studied for pattern classification. Most existing methods introduce multiple discriminative terms into objective functions for accuracy improvement, leading to complex learning frameworks and high computational burdens. This paper proposes a simple yet effective DL algorithm for classification, namely unidirectional representation dictionary learning (URDL). Unidirectional constraint is proposed to guide coefficient directions in the representation to be discriminative. Besides, direction-thresholding is proposed to exploit the direction property in the classification scheme. It suppresses the disturbance from undesired non-zero coefficients, and improves the representation discriminability. We adopt squared \ell _{2} -norm-based regularization for efficient coding, and systematically analyze the mechanism of the proposed method. Extensive experiments on five data sets are conducted, including object categorization, scene classification, face recognition, and fine-grained flower classification. The experimental results demonstrate that the proposed approach not only outperforms the state-of-the-art DL algorithms in terms of recognition accuracy significantly, but also exhibits a much higher computational efficiency.