approach with the DCT approach.

Synthetic Experiment
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![(a) Image with detected FAST keypoints used for synthetic experiment. (b) Example warped images centered around a keypoint of interest.[]{data-label="fig:syntDataThumb"}](SyntheticDataSetThumbs.pdf){width="0.8\columnwidth"}

In this experiment, the image in Fig. \[fig:syntDataThumb\](a) is used. Firstly, FAST corners are extracted, and a LP is learned for each corner, where each learning method is trained with the same parameters. Then, 100 test warps are applied to each corner to generate synthetically warped images (see Fig. \[fig:syntDataThumb\](b)). For each test warp, the estimated warp update for each method under evaluation is recorded. 10 iterations are used for the energy-based methods. For the learning-based methods, except specified otherwise, we train the LPs to handle translations between -1.0 to 1.0, whereas other parameters are trained to vary between -0.2 and 0.2. The RMSE of the warp updates over all corners, as well as the timing for each method is recorded.

![Comparison of Symbolic Linear Predictor with other Linear Predictors with different training settings. (a) Increasing the number of sample warps. (b) Increasing the number of pixels used. (c) Increasing the maximum parameter variation.[]{data-label="fig:synthetic_lp"}](synthetic_plot_hplanes.pdf){width="\columnwidth"}

We first compare our proposed approach with other LPs under different training settings:

-   **Number of sample warps**: 