4,596 research outputs found
A Singular Value Thresholding Algorithm for Matrix Completion
This paper introduces a novel algorithm to approximate the matrix with minimum
nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood
as the convex relaxation of a rank minimization problem and arises in many important
applications as in the task of recovering a large matrix from a small subset of its entries (the famous
Netflix problem). Off-the-shelf algorithms such as interior point methods are not directly amenable
to large problems of this kind with over a million unknown entries. This paper develops a simple
first-order and easy-to-implement algorithm that is extremely efficient at addressing problems in
which the optimal solution has low rank. The algorithm is iterative, produces a sequence of matrices
{X^k,Y^k}, and at each step mainly performs a soft-thresholding operation on the singular values
of the matrix Y^k. There are two remarkable features making this attractive for low-rank matrix
completion problems. The first is that the soft-thresholding operation is applied to a sparse matrix;
the second is that the rank of the iterates {X^k} is empirically nondecreasing. Both these facts allow
the algorithm to make use of very minimal storage space and keep the computational cost of each
iteration low. On the theoretical side, we provide a convergence analysis showing that the sequence
of iterates converges. On the practical side, we provide numerical examples in which 1,000 × 1,000
matrices are recovered in less than a minute on a modest desktop computer. We also demonstrate
that our approach is amenable to very large scale problems by recovering matrices of rank about
10 with nearly a billion unknowns from just about 0.4% of their sampled entries. Our methods are
connected with the recent literature on linearized Bregman iterations for ℓ_1 minimization, and we
develop a framework in which one can understand these algorithms in terms of well-known Lagrange
multiplier algorithms
Calibrated Elastic Regularization in Matrix Completion
This paper concerns the problem of matrix completion, which is to estimate a
matrix from observations in a small subset of indices. We propose a calibrated
spectrum elastic net method with a sum of the nuclear and Frobenius penalties
and develop an iterative algorithm to solve the convex minimization problem.
The iterative algorithm alternates between imputing the missing entries in the
incomplete matrix by the current guess and estimating the matrix by a scaled
soft-thresholding singular value decomposition of the imputed matrix until the
resulting matrix converges. A calibration step follows to correct the bias
caused by the Frobenius penalty. Under proper coherence conditions and for
suitable penalties levels, we prove that the proposed estimator achieves an
error bound of nearly optimal order and in proportion to the noise level. This
provides a unified analysis of the noisy and noiseless matrix completion
problems. Simulation results are presented to compare our proposal with
previous ones.Comment: 9 pages; Advances in Neural Information Processing Systems, NIPS 201
Low rank tensor recovery via iterative hard thresholding
We study extensions of compressive sensing and low rank matrix recovery
(matrix completion) to the recovery of low rank tensors of higher order from a
small number of linear measurements. While the theoretical understanding of low
rank matrix recovery is already well-developed, only few contributions on the
low rank tensor recovery problem are available so far. In this paper, we
introduce versions of the iterative hard thresholding algorithm for several
tensor decompositions, namely the higher order singular value decomposition
(HOSVD), the tensor train format (TT), and the general hierarchical Tucker
decomposition (HT). We provide a partial convergence result for these
algorithms which is based on a variant of the restricted isometry property of
the measurement operator adapted to the tensor decomposition at hand that
induces a corresponding notion of tensor rank. We show that subgaussian
measurement ensembles satisfy the tensor restricted isometry property with high
probability under a certain almost optimal bound on the number of measurements
which depends on the corresponding tensor format. These bounds are extended to
partial Fourier maps combined with random sign flips of the tensor entries.
Finally, we illustrate the performance of iterative hard thresholding methods
for tensor recovery via numerical experiments where we consider recovery from
Gaussian random measurements, tensor completion (recovery of missing entries),
and Fourier measurements for third order tensors.Comment: 34 page
Resolving single molecule structures with Nitrogen-vacancy centers in diamond.
We present theoretical proposals for two-dimensional nuclear magnetic resonance spectroscopy protocols based on Nitrogen-vacancy (NV) centers in diamond that are strongly coupled to the target nuclei. Continuous microwave and radio-frequency driving fields together with magnetic field gradients achieve Hartmann-Hahn resonances between NV spin sensor and selected nuclei for control of nuclear spins and subsequent measurement of their polarization dynamics. The strong coupling between the NV sensor and the nuclei facilitates coherence control of nuclear spins and relaxes the requirement of nuclear spin polarization to achieve strong signals and therefore reduced measurement times. Additionally, we employ a singular value thresholding matrix completion algorithm to further reduce the amount of data required to permit the identification of key features in the spectra of strongly sub-sampled data. We illustrate the potential of this combined approach by applying the protocol to a shallowly implanted NV center addressing a small amino acid, alanine, to target specific hydrogen nuclei and to identify the corresponding peaks in their spectra
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