title: Retargeted Least Squares Regression Algorithm.
abstract: This brief presents a framework of retargeted least squares regression (ReLSR) for multicategory classification. The core idea is to directly learn the regression targets from data other than using the traditional zero-one matrix as regression targets. The learned target matrix can guarantee a large margin constraint for the requirement of correct classification for each data point. Compared with the traditional least squares regression (LSR) and a recently proposed discriminative LSR models, ReLSR is much more accurate in measuring the classification error of the regression model. Furthermore, ReLSR is a single and compact model, hence there is no need to train two-class (binary) machines that are independent of each other. The convex optimization problem of ReLSR is solved elegantly and efficiently with an alternating procedure including regression and retargeting as substeps. The experimental evaluation over a range of databases identifies the validity of our method.
authors.forename: Xu-Yao
authors.initials: XY
authors.lastname: Zhang
authors.forename: Lingfeng
authors.initials: L
authors.lastname: Wang
authors.forename: Shiming
authors.initials: S
authors.lastname: Xiang
authors.forename: Cheng-Lin
authors.initials: CL
authors.lastname: Liu