Dimensionality Reduction
We developed methods for compressing high-dimensional signals that enable smooth interpolation and extrapolation between images, sounds, shapes, etc.
Background & Objective: It may take millions of bytes to accurately record biometric data such as the shape of one's face, but it only takes a few hundred bytes to describe how one's face differs from similar faces. The distribution of all likely faces is presumed to form a smooth low-dimensional manifold. We have developed methods to model this manifold from data samples and assign it a coordinate system with which we can encode (compress) and decode (decompress) new samples. Navigating on this manifold makes it possible to interpolate and extrapolate.
Technical Discussion: Given a few data samples (high dimensional vectors) and local distances between similar samples, we construct a convex optimization whose solution is an isometric mapping function taking the sample space into the low dimensional coordinate system.
Publications:
Technology Areas:
Algorithms
Artificial Intelligence
Computer Vision
Modification Date: May 28, 2009
