A manifold-aware ranking kernel (MARK) for information retrieval is described herein. The MARK is implemented by using supervised and unsupervised learning. MARK is ranking-oriented such that the relative comparison formulation directly targets on the ranking problem, making the approach optimal for information retrieval. MARK is also manifold-aware such that the algorithm is able to exploit information from ample unlabeled data, which helps to improve generalization performance, particularly when there are limited number of labeled constraints. MARK is nonlinear: as a kernel-based approach, the algorithm is able to lead to a highly non-linear metric which is able to model complicated data distribution.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of manifold-aware ranking kernel learning programmed in a memory of a device comprising: a. performing combined supervised kernel learning and unsupervised manifold kernel learning; and b. generating a non-linear kernel model. 2. The method of claim 1 wherein Bregman projection is utilized when performing the supervised kernel learning. 3. The method of claim 1 wherein unlabeled data is utilized in the unsupervised manifold kernel learning. 4. The method of claim 1 wherein the result comprises a non-linear metric defined by a kernel model. 5. The method of claim 1 wherein the supervised kernel learning employs a relative comparison constraint. 6. The method of claim 1 wherein the device is selected from the group consisting of a personal computer, a laptop computer, a computer workstation, a server, a mainframe computer, a handheld computer, a personal digital assistant, a cellular/mobile telephone, a smart phone, a smart appliance, a gaming console, a digital camera, a digital camcorder, a camera phone, an portable music player, a tablet computer, a video player, a DVD writer/player, a high definition video writer/player, a television and a home entertainment system. 7. A method of information retrieval programmed in a memory of a device comprising: a. receiving a search query input; b. performing a search based on the search query input and using a metric kernel learned by manifold-aware ranking kernel learning; and c. presenting a search result of the search. 8. The method of claim 7 wherein manifold-aware ranking kernel learning comprises: i. performing combined supervised kernel learning and unsupervised manifold kernel learning; and ii. generating a non-linear kernel model. 9. The method of claim 8 wherein Bregman projection is utilized when performing the supervised kernel learning. 10. The method of claim 8 wherein unlabeled data is utilized in the unsupervised manifold kernel learning. 11. The method of claim 8 wherein the result comprises a non-linear metric defined by a kernel model. 12. The method of claim 8 wherein the supervised kernel learning employs a relative comparison constraint. 13. The method of claim 7 wherein the search result comprises a set of entities from a database that are similar to the search query input. 14. The method of claim 7 wherein the device is selected from the group consisting of a personal computer, a laptop computer, a computer workstation, a server, a mainframe computer, a handheld computer, a personal digital assistant, a cellular/mobile telephone, a smart phone, a smart appliance, a gaming console, a digital camera, a digital camcorder, a camera phone, an portable music player, a tablet computer, a video player, a DVD writer/player, a high definition video writer/player, a television and a home entertainment system. 15. An apparatus comprising: a. a non-transitory memory for storing an application, the application for: i. performing combined supervised kernel learning and unsupervised manifold kernel learning; and ii. generating a non-linear kernel model; and b. a processing component coupled to the memory, the processing component configured for processing the application. 16. The apparatus of claim 15 wherein Bregman projection is utilized when performing the supervised kernel learning. 17. The apparatus of claim 15 wherein unlabeled data is utilized in the unsupervised manifold kernel learning. 18. The apparatus of claim 15 wherein the result comprises a non-linear metric defined by a kernel model. 19. The apparatus of claim 15 wherein the supervised kernel learning employs a relative comparison constraint. 20. An apparatus comprising: a. a non-transitory memory for storing an application, the application for: i. receiving a search query input; ii. performing a search based on the search query input and using a metric kernel learned by manifold-aware ranking kernel learning; and iii. presenting a search result of the search; and b. a processing component coupled to the memory, the processing component configured for processing the application. 21. The apparatus of claim 20 wherein manifold-aware ranking kernel learning comprises: i. performing combined supervised kernel learning and unsupervised manifold kernel learning; and ii. generating a non-linear kernel model. 22. The apparatus of claim 21 wherein Bregman projection is utilized when performing the supervised kernel learning. 23. The apparatus of claim 21 wherein unlabeled data is utilized in the unsupervised manifold kernel learning. 24. The apparatus of claim 21 wherein the result comprises a non-linear metric defined by a kernel model. 25. The apparatus of claim 21 wherein the supervised kernel learning employs a relative comparison constraint. 26. The apparatus of claim 20 wherein the search result comprises a set of entities from a database that are similar to the search query input.