mlsanityData to 7 Oct 2026

SRMD: Sparse Random Mode Decomposition

arXiv 2204.06108 ↗First posted 12 Apr 2022eess.SP cs.CV stat.MLcited by 6

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Abstract (arXiv)

Signal decomposition and multiscale signal analysis provide many useful tools for time-frequency analysis. We proposed a random feature method for analyzing time-series data by constructing a sparse approximation to the spectrogram. The randomization is both in the time window locations and the frequency sampling, which lowers the overall sampling and computational cost. The sparsification of the spectrogram leads to a sharp separation between time-frequency clusters which makes it easier to identify intrinsic modes, and thus leads to a new data-driven mode decomposition. The applications include signal representation, outlier removal, and mode decomposition. On the benchmark tests, we show that our approach outperforms other state-of-the-art decomposition methods.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 14 of its 49 references that are in the map. First posted on arXiv 12 Apr 2022.

Builds on

14 of 49 references.

References · 14

  1. 2018The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkscited by 2,250
  2. 2016Generalization Properties of Learning with Random Featurescited by 163
  3. 2015On the Equivalence between Kernel Quadrature Rules and Random Feature Expansionscited by 136
  4. 2021Generalization error of random features and kernel methods: hypercontractivity and kernel matrix concentrationcited by 110
  5. 2020Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don'tcited by 94
  6. 2018Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithmscited by 52
  7. 2017Sparse High-Dimensional Regression: Exact Scalable Algorithms and Phase Transitionscited by 43
  8. 2015Optimal Rates for Random Fourier Featurescited by 38
  9. 2021Generalization Bounds for Sparse Random Feature Expansionscited by 22
  10. 2017Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is lowcited by 17
  11. 2022HARFE: Hard-Ridge Random Feature Expansioncited by 12
  12. 2021SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruningcited by 9
2 more
  1. 2021Conditioning of Random Feature Matrices: Double Descent and Generalization Errorcited by 7
  2. 2014A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentationcited by 6

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Cited by year

Citing papers per year: 2022: 3; 2023: 1; 2024: 1; 2025: 1.

6 papers.

  1. 2022Random Feature Models for Learning Interacting Dynamical Systemscited by 10
  2. 2022Concentration of Random Feature Matrices in High-Dimensionscited by 8
  3. 2022SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemicscited by 4
  4. 2023Generalization Bound for Diffusion Models using Random Featurescited by 3
  5. 2025Cauchy Random Features for Operator Learning in Sobolev Spacecited by 3
  6. 2024Differentially Private Random Feature Modelcited by 2