Linear regression classification steered discriminative projection for dimension reduction

Because of the simplicity and effectiveness of linear regression classification (LRC), LRC is widely applied into image classification. However, it processes the original high-dimensional data directly. It is well known that the original data usually contains a lot of redundant information or noise, which will reduce the performance of LRC algorithm and increase its running cost. At the same time, it usually suffers from out of sample problem. In order to overcome the weaknesses of LRC, a novel dimension reduction algorithm termed linear regression classification steered discriminative projection (LRC-DP) is presented by combining LRC with discriminative projection. LRC-DP not only fits LRC well, but also seeks a linear projection, in which the ratio of between-class reconstruction errors to within-class reconstruction errors is maximized in the transformation space. The proposed LRC-DP can learn a robust low-dimensional projection subspace from the original sample images in high-dimension space. In order to validate the performance of LRC-DP algorithm, extensive experiments are conducted on several public image databases. Experimental results reveal that the LRC-DP algorithm is feasible and effective.