Center-based weighted kernel linear regression for image classification

Recently, the linear regression classification (LRC) has been proposed. LRC uses linear regression representation of each class for classification. Based on LRC, a few improved methods are proposed, such as kernel linear regression classification (KLRC), kernel ridge regression classification (KRRC) and nearest regularized subspace (NRS). Motivated by the LRC, KLRC, KRRC and NRS, center-based weighted kernel linear regression (CWKLR) is proposed for image classification in this paper. CWKLR firstly constitutes the center-based kernel matrix based on the KLRC and KRRC. Next, CWKLR utilizes the Tikhonov Matrix to solve the weighted projection coefficients for classification. Experiments on Coil100 object database, Eth80 object database and GT face database are used to evaluate the proposed algorithms. The experimental results demonstrate that the proposed methods achieve better recognition rate than some state-of-the-art methods.