FAQ

Regularization by spectral filtering

Spectral regularization is any of a class of regularization techniques used in machine learning to control the impact of noise and prevent… (More)
Wikipedia

Topic mentions per year

Topic mentions per year

1988-2016
02419882016

Related topics

Related topics

10 relations

Broader (1)

Computer engineering
Condition numberCross-validation (statistics)Gradient descentLandweber iteration
(More)

Related mentions per year

Related mentions per year

1937-2018
19401960198020002020
Regularization by spectral filtering
Machine learning
Principal component analysis
Matrix regularization
Gradient descent
Cross-validation (statistics)

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
Adversarial Dropout Regularization
We present a method for transferring neural representations from label-rich source domains to unlabeled target domains. Recent… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
2017
2017
c-LASSO and its dual for sparse signal estimation from array data
We treat the estimation of a sparse set of sources emitting plane waves observed by a sensor array as a complex-valued LASSO (c… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
2017
2017
Hyperspectral image restoration based on spatio-spectral structure tensor regularization
We propose a regularization function for hyperspectral image restoration based on a newly-designed structure tensor. We adopt a… (More)
  • figure 1
  • table I
  • table II
Is this relevant?
2015
2015
Matching Pursuit LASSO Part I: Sparse Recovery Over Big Dictionary
Large-scale sparse recovery (SR) by solving ℓ1-norm relaxations over Big Dictionary is a very challenging task. Plenty of greedy… (More)
  • figure 1
  • figure 2
  • table I
Is this relevant?
2014
2014
Convergence of online gradient method for feedforward neural networks with smoothing L1/2 regularization penalty
Minimization of the training regularization term has been recognized as an important objective for sparse modeling and… (More)
  • figure 1
  • table 1
  • figure 2
  • figure 3
  • figure 4
Is this relevant?
2012
2012
Nonlinear stochastic model predictive control via regularized polynomial chaos expansions
A new method to control stochastic systems in the presence of input and state constraints is presented. The method exploits a… (More)
  • table I
  • figure 1
  • figure 2
Is this relevant?
Highly Cited
2011
Highly Cited
2011
Topology and prediction of RNA pseudoknots
MOTIVATION Several dynamic programming algorithms for predicting RNA structures with pseudoknots have been proposed that differ… (More)
  • figure 1
  • figure 4
  • figure 3
  • figure 6
  • figure 8
Is this relevant?
2010
2010
Sparse learning for support vector classification
This paper provides a sparse learning algorithm for Support Vector Classification (SVC), called Sparse Support Vector… (More)
  • figure 1
  • figure 2
  • figure 4
  • figure 3
  • table 1
Is this relevant?
2007
2007
Optimization with Partial Differential Equations
In the rst part of this article, we have shown how time-dependent optimal control for partial di erential equations can be… (More)
Is this relevant?
1988
1988
Small-Angle-Scattering-Data Treatment by the Regularization Method
The application of Tikhonov's regularization method [Tikhonov & Arsenin (1977). Solution of Ill-Posed Problems. New York: Wiley… (More)
  • figure 1
  • figure 3
  • figure 4
  • figure 5
  • figure 6
Is this relevant?