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An Adaptive Sublinear-Time Block Sparse Fourier Transform

Volkan Cevher, Mikhail Kapralov, Jonathan Mark Scarlett, Amir Zandieh

The problem of approximately computing the

k

dominant Fourier coefficients of a vector

X

quickly, and using few samples in time domain, is known as the Sparse Fourier Transform (sparse FFT) problem. A long line of work on the sparse FFT has resulted in ...

2017

Acquisition comprimée

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Graph learning under sparsity priors

Fast Computation Of Spectral Projectors Of Banded Matrices

Gaussian versus Sparse Stochastic Processes

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Sparse inverse problems for Fourier imaging

Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Face Recognition in Real-world Images

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

Sparse Pronunciation Codes for Perceptual Phonetic Information Assessment

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Gaussian versus Sparse Stochastic Processes

Fast Computation Of Spectral Projectors Of Banded Matrices

Sparse inverse problems for Fourier imaging

Face Recognition in Real-world Images

Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

Sparse Pronunciation Codes for Perceptual Phonetic Information Assessment

Graph learning under sparsity priors