Semi-supervised multi-output image manifold regression

We present a data-driven method for semi-supervised multioutput regression on image manifolds, which simultaneously considers the manifold structure of the input data and complex output labels. Compared to related methods, our method achieves superior prediction accuracy on a variety of data sets, with as few as 5% of the input examples labeled. Also, with a few labeled examples and no domain-specific tuning, our method performs on par with specialized algorithms for tasks such as face landmark detection.