We consider the task of dimensionality reduction for regression (DRR) whose goal is to find a low dimensional representation of input covariates, while preserving the statistical ...
We introduce a number of new results in the context of multi-view geometry from general algebraic curves. We start with the derivation of the extended Kruppa's equations whic...
Jeremy Yermiyahou Kaminski, Michael Fryers, Amnon ...
Abstract. Compressive sensing (CS) is an emerging field that provides a framework for image recovery using sub-Nyquist sampling rates. The CS theory shows that a signal can be reco...
Volkan Cevher, Aswin C. Sankaranarayanan, Marco F....
This paper explores the direct motion estimation problem assuming that video-rate depth information is available, from either stereo cameras or other sensors. We use these depth m...
Michael Harville, Ali Rahimi, Trevor Darrell, Gail...
We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...