129 research outputs found
Random Subsets of Structured Deterministic Frames have MANOVA Spectra
We draw a random subset of rows from a frame with rows (vectors) and
columns (dimensions), where and are proportional to . For a
variety of important deterministic equiangular tight frames (ETFs) and tight
non-ETF frames, we consider the distribution of singular values of the
-subset matrix. We observe that for large they can be precisely
described by a known probability distribution -- Wachter's MANOVA spectral
distribution, a phenomenon that was previously known only for two types of
random frames. In terms of convergence to this limit, the -subset matrix
from all 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
-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 frame vectors out of
possible vectors, and has a variety of implications for erasure coding,
compressed sensing, and sparse recovery. When the aspect ratio is small,
the MANOVA spectrum tends to the well known Marcenko-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
Generic MANOVA limit theorems for products of projections
We study the convergence of the empirical spectral distribution of
for orthogonal projection
matrices and , where
and
converge as , to Wachter's MANOVA law. Using free probability, we
show mild sufficient conditions for convergence in moments and in probability,
and use this to prove a conjecture of Haikin, Zamir, and Gavish (2017) on
random subsets of unit-norm tight frames. This result generalizes previous ones
of Farrell (2011) and Magsino, Mixon, and Parshall (2021). We also derive an
explicit recursion for the difference between the empirical moments
and the limiting
MANOVA moments, and use this to prove a sufficient condition for convergence in
probability of the largest eigenvalue of to
the right edge of the support of the limiting law in the special case where
that law belongs to the Kesten-McKay family. As an application, we give a new
proof of convergence in probability of the largest eigenvalue when
is unitarily invariant; equivalently, this determines the limiting operator
norm of a rectangular submatrix of size of a
Haar-distributed unitary matrix for any .
Unlike previous proofs, we use only moment calculations and non-asymptotic
bounds on the unitary Weingarten function, which we believe should pave the way
to analyzing the largest eigenvalue for products of random projections having
other distributions.Comment: 49 pages, 1 figur
Models and Analysis of Vocal Emissions for Biomedical Applications
The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy
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