Methods and systems for creating ensembles of hypersurfaces in high-dimensional feature spaces, and to machines and systems relating thereto. More specifically, exemplary aspects of the invention relate to methods and systems for generating supervised hypersurfaces based on user domain expertise, machine learning techniques, or other supervised learning techniques. These supervised hypersurfaces may optionally be combined with unsupervised hypersurfaces derived from unsupervised learning techniques. Lower-dimensional subspaces may be determined by the methods and systems for creating ensembles of hypersurfaces in high-dimensional feature spaces. Data may then be projected onto the lower-dimensional subspaces for use, e.g., in further data discovery, visualization for display, or database access. Also provided are tools, systems, devices, and software implementing the methods, and computers embodying the methods and/or running the software, where the methods, software, and computers utilize various aspects of the present invention relating to analyzing data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for analysis of a high-dimensional feature space comprising: generating a supervised hypersurface using supervised learning techniques; optionally generating an unsupervised hypersurface using unsupervised learning techniques; combining the supervised hypersurface and optional unsupervised hypersurface to create a lower-dimensional subspace; projecting data from the high-dimensional feature space onto the lower-dimensional subspace; and outputting the projected data into a computer memory. 2. The method of claim 1, wherein the subspace is defined on an orthonormal basis. 3. The method of claim 1, wherein the lower-dimensional subspace comprises a new model of the data from the high-dimensional feature space. 4. The method of claim 1, wherein the projected data is used to conduct further analysis of the projected data. 5. The method of claim 4, wherein the further analysis is selected from the group consisting of data discovery, data display, and database exploration. 6. The method of claim 1, wherein the projected data is used to generate a visual data display on a display means. 7. The method of claim 1, wherein the data projected onto the lower dimensional subspace is used to represent an svm or learning machine. 8. The method of claim 7, wherein the data projected onto the lower dimensional subspace is used to represent a combination of an svm or learning machine and an unsupervised learning method in a single visual subspace model. 9. The method of claim 1, wherein the supervised learning techniques is a machine learning solution. 10. A computer program product for analysis of a high-dimensional feature space, comprising: first computer readable program code means for generating a supervised hypersurface using supervised learning techniques; second optional computer readable program code means for generating an unsupervised hypersurface using unsupervised learning techniques; third computer readable program code means for combining the supervised hypersurface and optional unsupervised hypersurface to create a lower-dimensional subspace; fourth computer readable program code means for projecting data from the high-dimensional feature space onto the lower-dimensional subspace; and fifth computer readable program code means for outputting the projected data on an output device. 11. A method for projecting high-dimensional data from a high-dimensional data space onto a lower-dimensional subspace comprising: generating one or more axes from high-dimensional data, wherein said axes include at least one vector chosen from the following: a vector normal to a hypersurface in said high-dimensional data space, said hypersurface being derived using supervised means, a vector created from a hypothetical data pattern, or a vector selected from an actual data pattern; optionally generating one or more axes from high-dimensional data, wherein said axes include at least one vector normal to an unsupervised hypersurface in said high-dimensional data space, said unsupervised hypersurface being derived using unsupervised means; projecting the high-dimensional data onto said vectors to form a lower-dimensional subspace; and outputting the lower-dimensional subspace into a computer memory. 12. The method of claim 11, further comprising orthonormalizing the vectors with respect to one another. 13. The method of claim 11, wherein the projected data is used to generate a visual data display on a display means. 14. The method of claim 11, wherein the data projected onto the lower dimensional subspace is used to represent an svm or learning machine, or a combination of an svm or learning machine and an unsupervised learning method. 15. The method of claim 11, wherein the supervised means are machine learning techniques. 16-22. (canceled) 23. The method of claim 1, wherein hypothetical or actual data patterns are used to create a lower-dimensional subspace. 24. The method of claim 11, wherein the projected data is used to conduct further analysis of the projected data. 25. The method of claim 11, wherein the further analysis is selected from the group consisting of data discovery, data display, and database exploration.