SRMD: sparse random mode decomposition

Fourier and Fourier-Stieltjes transforms and other transforms of Fourier type (42B10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Numerical methods for discrete and fast Fourier transforms (65T50)

Cites Work

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A mathematical introduction to compressive sensing
A branch-and-cut decomposition algorithm for solving chance-constrained mathematical programs with finite support
Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool
On the computational power of circuits of spiking neurons
Generalization bounds for sparse random feature expansions
Generalization error of random feature and kernel methods: hypercontractivity and kernel matrix concentration
Sparse high-dimensional regression: exact scalable algorithms and phase transitions
Synchrosqueezed wave packet transforms and diffeomorphism based spectral analysis for 1D general mode decompositions
2D Empirical Transforms. Wavelets, Ridgelets, and Curvelets Revisited
ADAPTIVE DATA ANALYSIS VIA SPARSE TIME-FREQUENCY REPRESENTATION
Synchrosqueezing-Based Recovery of Instantaneous Frequency from Nonuniform Samples
The Split Bregman Method for L1-Regularized Problems
The Perceptron: A Model for Brain Functioning. I
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Empirical Wavelet Transform
Variational Mode Decomposition
On Recovery of Sparse Signals Via $\ell _{1}$ Minimization
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms
Scalable Algorithms for the Sparse Ridge Regression
A parameterless scale-space approach to find meaningful modes in histograms — Application to image and spectrum segmentation
On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions
HARFE: hard-ridge random feature expansion
Subset Selection with Shrinkage: Sparse Linear Modeling When the SNR Is Low

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