期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2017
卷号：114
期号：26
页码：E5024-E5033
DOI：10.1073/pnas.1700203114
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：We draw a random subset of k rows from a frame with n rows (vectors) and m columns (dimensions), where k and m are proportional to n . For a variety of important deterministic equiangular tight frames (ETFs) and tight non-ETFs, we consider the distribution of singular values of the k -subset matrix. We observe that, for large n , they can be precisely described by a known probability distribution—Wachter’s MANOVA (multivariate ANOVA) spectral distribution, a phenomenon that was previously known only for two types of random frames. In terms of convergence to this limit, the k -subset matrix from all of these frames is shown to be empirically indistinguishable from the classical MANOVA (Jacobi) random matrix ensemble. Thus, empirically, the MANOVA ensemble offers a universal description of the spectra of randomly selected k subframes, even those taken from deterministic frames. The same universality phenomena is shown to hold for notable random frames as well. This description enables exact calculations of properties of solutions for systems of linear equations based on a random choice of k frame vectors of n possible vectors and has a variety of implications for erasure coding, compressed sensing, and sparse recovery. When the aspect ratio m / n is small, the MANOVA spectrum tends to the well-known Marčenko–Pastur distribution of the singular values of a Gaussian matrix, in agreement with previous work on highly redundant frames. Our results are empirical, but they are exhaustive, precise, and fully reproducible.
