NuMax: A Convex Approach for Learning Near-Isometric Linear Embeddings

We propose a novel framework for the deterministic construction of linear, near-isometric embeddings of a finite set of data points. Given a set of training points X ⊂ \BBR ^N , we consider the secant set S(X) that consists of all pairwise difference vectors of X, normalized to lie on the unit sphere. We formulate an affine rank minimization...