Fig. 2.

Performance of the method on synthetic dataset. Left: The figure demonstrates the models functionality by effectively shutting down excessive views to prune the search space, and its ability to identify the features weights correctly. The true weights corresponding to the four views are shown along with the weights learned by our model and elastic net regression. The view-sparsity in MVLR shuts down the irrelevant views. Right: Prediction performance of our model and the comparison approach when the number of sample size is varied. Each point represents the average prediction performance over 50 experiments with error bars indicating one standard error over the mean. The structured sparsity assumptions of our model are especially beneficial when the sample sizes are small in comparison to the number of dimensions