Abstract: We propose an approach for linear unsupervised dimensionality reduction, based
on the sparse linear model that has been used to probabilistically interpret sparse
coding. We formulate an optimization problem for learning a linear projection
from the original signal domain to a lower-dimensional one in a way that approxi-
mately preserves, in expectation, pairwise inner products in the sparse domain. We
derive solutions to the problem, present nonlinear extensions, and discuss relations
to compressed sensing. Our experiments using facial images, texture patches, and
images of object categories suggest that the approach can improve our ability to
recover meaningful structure in many classes of signals.
Paper: [pdf] [bibtex] [supplemental] [slides]
Code: coming soon.
Contact: igkiou [at] seas [dot] harvard [dot] edu
