1. A method for analysis of a high-dimensional feature space comprising labelled data, comprising: generating a first supervised hypersurface and a first vector normal to the first hypersurface using supervised learning techniques on said labelled data; generating a second unsupervised hypersurface and a second vector normal to the second hypersurface using unsupervised learning techniques on said labelled data after removing the labels; selecting a subspace comprising the supervised hypersurface and unsupervised hypersurface; projecting data from the high-dimensional feature space onto the orthonormal basis that spans the selected subspace comprising the first vector normal to the first hypersurface and the second vector normal to the second hypersurface; and outputting the projected data into a computer memory.