Fetching the paper…
Reading the bibliography…
Signal decomposition and multiscale signal analysis provide many useful tools for time-frequency analysis.
The perceptron: A model for brain functioning. i
Hans-Dieter Block · 1962
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
Earlier work this paper cites.
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E Huang, Zheng Shen, Steven R Long, Manli C Wu, Hsing H Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung, and Henry H Liu · 1998
Earlier work this paper cites.
Empirical mode decomposition as a filter bank
Patrick Flandrin, Gabriel Rilling, and Paulo Goncalves · 2004
Earlier work this paper cites.
On the computational power of circuits of spiking neurons
Wolfgang Maass and Henry Markram · 2004
Earlier work this paper cites.
Near-optimal signal recovery from random projections: Universal encoding strategies?
Emmanuel J Candes and Terence Tao · 2006
Earlier work this paper cites.
Randomized clustering forests for building fast and discriminative visual vocabularies
Frank Moosmann, B Triggs, and Frederic Jurie · 2006
Earlier work this paper cites.
Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
Earlier work this paper cites.
A linear time histogram metric for improved sift matching
Ofir Pele and Michael Werman · 2008
Earlier work this paper cites.
Uniform approximation of functions with random bases
Ali Rahimi and Benjamin Recht · 2008
Earlier work this paper cites.
Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
Ali Rahimi and Benjamin Recht · 2008
Earlier work this paper cites.
On recovery of sparse signals via ℓ 1 \ell^{1} minimization
T Tony Cai, Guangwu Xu, and Jun Zhang · 2009
Earlier work this paper cites.
The split Bregman method for L1-regularized problems
Tom Goldstein and Stanley Osher · 2009
Earlier work this paper cites.
Fast and robust earth mover’s distances
Ofir Pele and Michael Werman · 2009
Earlier work this paper cites.
Ensemble empirical mode decomposition: a noise-assisted data analysis method
Zhaohua Wu and Norden E Huang · 2009
Earlier work this paper cites.
Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool
Ingrid Daubechies, Jianfeng Lu, and Hau-Tieng Wu · 2011
Earlier work this paper cites.
Adaptive data analysis via sparse time-frequency representation
Thomas Y Hou and Zuoqiang Shi · 2011
Earlier work this paper cites.
Synchrosqueezing-based recovery of instantaneous frequency from nonuniform samples
Gaurav Thakur and Hau-Tieng Wu · 2011
Cited alongside, same era.
A complete ensemble empirical mode decomposition with adaptive noise
María E Torres, Marcelo A Colominas, Gastón Schlotthauer, and Patrick Flandrin · 2011
Cited alongside, same era.
Time-frequency reassignment and synchrosqueezing: An overview
François Auger, Patrick Flandrin, Yu-Ting Lin, Stephen McLaughlin, Sylvain Meignen, Thomas Oberlin, and Hau-Tieng Wu · 2013
Cited alongside, same era.
Variational mode decomposition
Konstantin Dragomiretskiy and Dominique Zosso · 2013
Cited alongside, same era.
A Mathematical Introduction to Compressive Sensing
Simon Foucart and Holger Rauhut · 2013
Cited alongside, same era.
Empirical wavelet transform
Jérôme Gilles · 2013
Cited alongside, same era.
Generalization properties of learning with random features
Alessandro Rudi and Lorenzo Rosasco · 2017
Later among the works it cites.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
Later among the works it cites.
Statistical learning with sparsity: the lasso and generalizations
Trevor Hastie, Robert Tibshirani, and Martin Wainwright · 2019
Later among the works it cites.
Towards a unified analysis of random Fourier features
Zhu Li, Jean-Francois Ton, Dino Oglic, and Dino Sejdinovic · 2019
Later among the works it cites.
Recent advancements in empirical wavelet transform and its applications
Wei Liu and Wei Chen · 2019
Later among the works it cites.
Sparse high-dimensional regression: Exact scalable algorithms and phase transitions
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications
Gaurav Thakur, Eugene Brevdo, Neven S Fučkar, and Hau-Tieng Wu · 2013
Cited alongside, same era.
A parameterless scale-space approach to find meaningful modes in histograms—Application to image and spectrum segmentation
Jérôme Gilles, Kathryn Heal · 2014
Cited alongside, same era.
2D empirical transforms. wavelets, ridgelets, and curvelets revisited
Jérôme Gilles, Giang Tran, and Stanley Osher · 2014
Cited alongside, same era.
A branch-and-cut decomposition algorithm for solving chance-constrained mathematical programs with finite support
James Luedtke · 2014
Cited alongside, same era.
Synchrosqueezing s-transform and its application in seismic spectral decomposition
Zhong-lai Huang, Jianzhong Zhang, Tie-hu Zhao, and Yunbao Sun · 2015
Cited alongside, same era.
Optimal rates for random fourier features
Bharath Kumar Sriperumbudur and Zoltan Szabo · 2015
Cited alongside, same era.
Dimitris Bertsimas and Bart Van Parys · 2020
Later among the works it cites.
Evaluating five different adaptive decomposition methods for EEG signal seizure detection and classification
Vinícius R Carvalho, Márcio FD Moraes, Antônio P Braga, and Eduardo MAM Mendes · 2020
Later among the works it cites.
Weinan E, Chao Ma, Stephan Wojtowytsch, and Lei Wu · 2020
Later among the works it cites.
Fast best subset selection: Coordinate descent and local combinatorial optimization algorithms
Hussein Hazimeh and Rahul Mazumder · 2020
Later among the works it cites.
ssqueezepy, 2020
John Muradeli · 2020
Later among the works it cites.
Scalable algorithms for the sparse ridge regression
Weijun Xie and Xinwei Deng · 2020
Later among the works it cites.
Conditioning of random feature matrices: Double descent and generalization error
Zhijun Chen and Hayden Schaeffer · 2021
Later among the works it cites.
Generalization bounds for sparse random feature expansions
Abolfazl Hashemi, Hayden Schaeffer, Robert Shi, Ufuk Topcu, Giang Tran, and Rachel Ward · 2021
Later among the works it cites.
Song Mei, Theodor Misiakiewicz, and Andrea Montanari · 2021
Later among the works it cites.
SHRIMP: Sparser random feature models via iterative magnitude pruning
Yuege Xie, Bobby Shi, Hayden Schaeffer, and Rachel Ward · 2021
Later among the works it cites.
HARFE: Hard-ridge random feature expansion
Esha Saha, Hayden Schaeffer, and Giang Tran · 2022
Closest in time.