7. An apparatus for performing semisupervised learning on a data set, comprising: a memory adapted to store a data set, comprising labeled data and unlabeled data; at least one automated processor, configured to analyze the data set through a parametric function derived by principal component analysis of the feature space reflecting a geometric structure of a marginal distribution of the data set according to its principal components, and performing supervised learning on the labeled data based on the parametric function derived by principal component analysis of the feature space reflecting the geometric structure of the entire data set; and a memory adapted to store information derived from said supervised learning in a computer memory.
7. An apparatus for performing semisupervised learning on a data set, comprising: a memory adapted to store a data set, comprising labeled data and unlabeled data; at least one automated processor, configured to analyze the data set through a parametric function derived by principal component analysis of the feature space reflecting a geometric structure of a marginal distribution of the data set according to its principal components, and performing supervised learning on the labeled data based on the parametric function derived by principal component analysis of the feature space reflecting the geometric structure of the entire data set; and a memory adapted to store information derived from said supervised learning in a computer memory. 10. The apparatus according to claim 7 , wherein the automated processor performs a binary classification of unlabeled data based on the stored supervised learning information.