A Well-Tempered Landscape for Non-convex Robust Subspace Recovery

06/13/2017

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by   Tyler Maunu, et al.

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University of Minnesota

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MIT

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University of Central Florida

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We present a mathematical analysis of a non-convex energy landscape for Robust Subspace Recovery. We prove that an underlying subspace is the only stationary point and local minimizer in a large neighborhood if a generic condition holds for a dataset. We further show that if the generic condition is satisfied, a geodesic gradient descent method over the Grassmannian manifold can exactly recover the underlying subspace with proper initialization. The condition is shown to hold with high probability for a certain model of data.