the pre-computation step is usually computationally expensive. Thus, learning-based approaches have so far only been used for a single large image patch consisting of a sub-sampled grid of points. Holzer *et al.* [@Holzer2015] proposed methods to make the pre-computation step more efficient. However, the computational savings obtained from this method do not translate well to the problem of subpixel refinement. Further, in an image, an additional challenge is that there are typically hundreds of keypoints of interest.

In this paper, we present an approach which makes the learning step efficient for subpixel refinement. We propose to divide the learning step into two stages. Firstly, we perform a once-off step which learns a symbolic representation of the Linear Predictor. Once learned, it can be re-used multiple times for different image patches. Our approach is very efficient for small image patches, thus making it suitable for subpixel refinement. We envision our approach being used in situations where Linear Predictors for multiple keypoints are learned efficiently in the background process of a real-time application such as visual odometry (VO) or SLAM. Further, we propose an error measure which allows the expected error in the warp parameters of an image patch to be predicted. 