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Random Projections for Manifold Learning
Chinmay Hegde · Richard Baraniuk

Mon Dec 03 10:30 AM -- 10:40 AM (PST) @ None #None
We propose a novel method for {\em linear} dimensionality reduction of manifold modeled data. First, we show that with a small number $M$ of {\em random projections} of sample points in $\reals^N$ belonging to an unknown $K$-dimensional Euclidean manifold, the intrinsic dimension (ID) of the sample set can be estimated to high accuracy. Second, we rigorously prove that using only this set of random projections, we can estimate the structure of the underlying manifold. In both cases, the number random projections required is linear in $K$ and logarithmic in $N$, meaning that $K

Author Information

Chinmay Hegde (Rice University)
Richard Baraniuk (Rice University)

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