Label consistent recursive least squares dictionary learning for image classification
Sergio Matiz, Kenneth E. Barner · 2016
A label consistent recursive least squares dictionary learning algorithm, LC-RLSDLA, is proposed to learn discriminative dictionaries for image classification based on sparse coding. The class label information and a label consistency term are used in the cost function to enforce discriminability among the sparse codes. Two operation modes are derived for the LC-RLSDLA: the supervised learning mode, in which the algorithm employs a training set to learn the dictionary and linear classifier simultaneously, and the online semi-supervised learning mode, in which the algorithm continuously learns as it classifies new vectors. Experiments performed on two face recognition databases demonstrate that the proposed method outperforms state-of-the-art sparse coding algorithms.