1. A semisupervised learning method, comprising: analyzing a data set using at least one automated processor, comprising labeled data and unlabeled data, by performing a principal component analysis to derive parameters of a parametric function of the feature space reflecting a geometric structure of a marginal distribution of the data set according to its principal components; performing supervised learning on the labeled data using the at least one automated processor, based on the parametric function of the feature space reflecting the geometric structure of the marginal distribution of the entire data set; and storing information derived from said supervised learning in a computer memory, wherein the parametric function is dependent on both the data set and said principal component analysis.