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## Orthogonal Procrustes Analysis | SpringerLink

Silverman, Bernard W. Cai, T. Tony; Liang, Tengyuan; Rakhlin, Alexander. Geometric inference for general high-dimensional linear inverse problems. More by T. Abstract Article info and citation First page References Supplemental materials Abstract This paper presents a unified geometric framework for the statistical analysis of a general ill-posed linear inverse model which includes as special cases noisy compressed sensing, sign vector recovery, trace regression, orthogonal matrix estimation and noisy matrix completion.

Article information Source Ann. Export citation. Export Cancel. References [1] Amelunxen, D.