We consider the problem of estimating the locations of a set of points in a k-dimensional euclidean space given a subset of the pairwise distance measurements between the points. We focus on the case when some fraction of these measurements can be arbitrarily corrupted by large additive noise. Given that the problem is highly non-convex, we propose a simple semidefinite programming relaxation that can be efficiently solved using standard algorithms. ...
We consider the problem of estimating the locations of a set of points in a k-dimensional euclidean space given a subset of the pairwise distance measurements between the points. We focus on the case when some fraction of these measurements can be arbitrarily corrupted by large additive noise. This is motivated by applications like sensor networks, molecular conformation and manifold learning where the measurement process can induce large bias errors ...
It is highly desirable to have a video coding framework that can flexibly distributed coding complexity between the video encoder and decoder. However, today's video compression techniques impose a rigid computational complexity distribution between the video encoder and decoder. Specifically, video decoders are very simple but encoders are computationally complex as they need to carry out motion estimation and mode decision. Distributed video coding has shown great potentials in enabling ...
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