Random Projections for Manifold Learning: Proofs and Analysis
| Title | Random Projections for Manifold Learning: Proofs and Analysis |
| Publication Type | Report |
| Authors | C. Hegde, M. B. Wakin, and R. G. Baraniuk |
| Abstract | We derive theoretical bounds on the performance of manifold learning algorithms, given access to a small number of random projections of the input dataset. We prove that with the number of projections only logarithmic in the size of the original space, we may reliably learn the structure of the nonlinear manifold, as compared to performing conventional manifold learning on the full dataset. |
| Year of Publication | 2007 |
| Month | Oct. |
| Technical Report Number | TREE0710 |
| Institution | Rice University, Department of Electrical and Computer Engineering |
| Acknowledgements | This work was supported by research grants DARPA/ONR N66001-06-1-2011 and N00014-06-1-0610, NSF CCF-0431150, ONR N00014-07-1-0936, AFOSR FA9550-07-1-0301, ARO W911NF-07-1-0502, ARO MURI W311NF-07-1-0185, and the Texas Instruments Leadership University Program. |
Publication File:
Research project:
Random projections for manifold learning