Sparse regression
Adaptive multi-class Bayesian sparse regression - An application to brain activity classification
... regularized regression techniques have been developed, forcing the majority of the features to have zero or close to zero loadings, such as Lasso [?] and elastic net [?]; however, these approaches require that the ...
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Safe screening for sparse regression with the Kullback-Leibler divergence
... ABSTRACT Safe screening rules are powerful tools to accelerate iterative solvers in sparse regression problems. They allow early iden- tification of inactive coordinates (i.e., those not belonging to the ...
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Improved Local Spectral Unmixing of hyperspectral data using an algorithmic regularization path for collaborative sparse regression
... sand) [3]. Spectral variability [4, 5] can affect the endmembers’ sig- natures locally depending on the geometry of the scene (topography and changing illumination conditions) or because of changes in the ...
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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
... There is a great deal of work yet to be done; the list of ideas could be several pages long! A few directions are mentioned here. Proper representations of endmember distributions need to be identiÞed. Researchers have ...
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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
... There is a great deal of work yet to be done; the list of ideas could be several pages long! A few directions are mentioned here. Proper representations of endmember distributions need to be identified. Researchers have ...
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Sparse Regression Learning by Aggregation and Langevin Monte-Carlo
... of sparse recovery, ...-based sparse recovery is that in Theorem 2, we have no assumption on the dictionary {φ 1 , ...logistic regression the link function g, as well as its first two derivatives, ...
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Deciphering splicing with sparse regression techniques in the era of high-throughput RNA sequencing.
... from 4.3(b) . Whereas the decomposition might not be ambiguous when f ? is a sum of few (s, t)-path flows, it is not unique in general. Our approach to flow decomposition consists of finding an (s, t)-path carrying the ...
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Sparse high dimensional regression in the presence of colored heteroscedastic noise: application to M/EEG source imaging
... B.2.4 Results on joint M/EEG real data We now evaluate our estimator on magneto- and electroencephalography (M/EEG) data. The data consists of M/EEG recordings, which measure the electric potential and magnetic field ...
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On combining wavelets expansion and sparse linear models for Regression on metabolomic data and biomarker selection
... a regression method often leads to deteriorated ...that sparse regression methods, applied in the wavelet domain, perform an automatic thresholding: the most relevant wavelet coefficients are ...
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Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression
... Several statistical contributions have tried to address heteroscedastic models in high dimensional regression. Most works have relied on an exponential representa- tion of the variance (the log-variance being ...
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Sparse high dimensional regression in the presence of colored heteroscedastic noise : application to M/EEG source imaging
... B.2.4 Results on joint M/EEG real data We now evaluate our estimator on magneto- and electroencephalography (M/EEG) data. The data consists of M/EEG recordings, which measure the electric potential and magnetic field ...
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Extremile Regression
... quartiles x of X. The plot shows stability over the region k P r50, 90s, which suggests to pick out the pointwise estimates p γpxq over this interval. The top right panel plots the final estimates p γpxq versus x ...
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A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine
... * Correspondence: tzwang@shmtu.edu.cn; Tel.: +86-2138-282-640 Received: 15 January 2019; Accepted: 7 February 2019; Published: 17 February 2019 Abstract: The development and application of marine ...
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Sparse sets
... 7. Using sparse sets in error analysis Now several results about robustness may be reformulated in terms of sparse sets. Let us give an example from percolation theory. Consider a grid of vertical and ...
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Blind regression : nonparametric regression for latent variable models via collaborative filtering
... their corresponding rating 𝑦(𝑢, 𝑖) is a noisy measurement of 𝑓 (𝑥 1 (𝑢), 𝑥 2 (𝑖)) for some unknown function 𝑓 . In contrast to classical regression, the features 𝑥 = (𝑥 1 (𝑢), 𝑥 2 (𝑖)) are not observed (latent), ...
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Quaternionic Sparse Approximation
... In this paper, we first consider sparse approximation and the OMP algorithm in Section 2. We then present the quaternionic extension Q-OMP in Section 3. In Sec- tion 4, we specify our work for the shift-invariant ...
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Sparse stochastic bandits
... d in the minimax analysis. Since upper and lower bounds match, this is actually ineluctable. On the other hand, we aim at leveraging an additional assumption to reduce that (linear) dependency in d and even get rid of ...
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Sparse polynomial interpolation: sparse recovery, super resolution, or Prony?
... naive sparse recovery LP-approach does not offer such a ...for sparse interpolation, which also recovers the exact decomposition but from less point evaluations and with no geometric spacing ...
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en fr Regression modelling of competing risks Modèles de regression en présence de compétition
... This could result in differences in estimates of treatment effect, due to the fact that patients who experienced the competing risks outcome will belong to all risk sets of the Fine and [r] ...
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Nonparametric Instrumental Regression
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