3,198 research outputs found
Alternating Least-Squares for Low-Rank Matrix Reconstruction
For reconstruction of low-rank matrices from undersampled measurements, we
develop an iterative algorithm based on least-squares estimation. While the
algorithm can be used for any low-rank matrix, it is also capable of exploiting
a-priori knowledge of matrix structure. In particular, we consider linearly
structured matrices, such as Hankel and Toeplitz, as well as positive
semidefinite matrices. The performance of the algorithm, referred to as
alternating least-squares (ALS), is evaluated by simulations and compared to
the Cram\'er-Rao bounds.Comment: 4 pages, 2 figure
Off-The-Grid Spectral Compressed Sensing With Prior Information
Recent research in off-the-grid compressed sensing (CS) has demonstrated
that, under certain conditions, one can successfully recover a spectrally
sparse signal from a few time-domain samples even though the dictionary is
continuous. In this paper, we extend off-the-grid CS to applications where some
prior information about spectrally sparse signal is known. We specifically
consider cases where a few contributing frequencies or poles, but not their
amplitudes or phases, are known a priori. Our results show that equipping
off-the-grid CS with the known-poles algorithm can increase the probability of
recovering all the frequency components.Comment: 5 pages, 4 figure
On the Power of Adaptivity in Matrix Completion and Approximation
We consider the related tasks of matrix completion and matrix approximation
from missing data and propose adaptive sampling procedures for both problems.
We show that adaptive sampling allows one to eliminate standard incoherence
assumptions on the matrix row space that are necessary for passive sampling
procedures. For exact recovery of a low-rank matrix, our algorithm judiciously
selects a few columns to observe in full and, with few additional measurements,
projects the remaining columns onto their span. This algorithm exactly recovers
an rank matrix using observations,
where is a coherence parameter on the column space of the matrix. In
addition to completely eliminating any row space assumptions that have pervaded
the literature, this algorithm enjoys a better sample complexity than any
existing matrix completion algorithm. To certify that this improvement is due
to adaptive sampling, we establish that row space coherence is necessary for
passive sampling algorithms to achieve non-trivial sample complexity bounds.
For constructing a low-rank approximation to a high-rank input matrix, we
propose a simple algorithm that thresholds the singular values of a zero-filled
version of the input matrix. The algorithm computes an approximation that is
nearly as good as the best rank- approximation using
samples, where is a slightly different coherence parameter on the matrix
columns. Again we eliminate assumptions on the row space
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