In this paper, the authors provide a methodology to design nonparametric permutation tests and, in particular, nonparametric rank tests for applications in detection. In the first part of the paper, the authors develop the optimization theory of both permutation and rank tests in the Neyman-Pearson sense; in the second part of the paper, they carry out a comparative performance analysis of the ...

This article focuses on the problem of reconstructing low-rank matrices from underdetermined measurements using alternating optimization strategies. We endeavour to combine an alternating least-squares based estimation strategy with ideas from the alternating direction method of multipliers (ADMM) to recover structured low-rank matrices, such as Hankel structure. We show that merging these two ...

We consider the recovery of a low rank real-valued matrix M given a subset of noisy discrete (or quantized) measurements. Such problems arise in several applications such as collaborative filtering, learning and content analytics, and sensor network localization. We consider constrained maximum likelihood estimation of M , under a constraint on the entrywise infinity-norm of M and an exact rank...

Thepaper describes the rank 1weighted factorization solution to the structure frommotionproblem. Thismethod recovers the 3Dstructure from the factorization of a datamatrix that is rank 1 rather than rank 3. Thismatrix collects the estimates of the 2Dmotions of a set of feature points of the rigid object. These estimates are weighted by the inverse of the estimates error standard deviation so th...