A lower bound on the error in dimensionality reduction resulting from projection onto a restricted subspace.
We obtain the lower bound on a variant of the common problem of dimensionality reduction. In this version, the dataset is projected on to a k dimensional subspace with the property that the first k-1 basis vectors are fixed, leaving a single degree of freedom in terms of basis vectors.
Department of Computer Science, Dartmouth College, Hanover NH 03755.
Himanshu
H
Chandola