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https://papers.nips.cc/paper/4062-spectral-regularization-for-support-estimation.pdf
not an isolated point of the spectrum, so that the estimation of a null space is an ill-posed problem (see for example [9]). Then, a regularization approach is needed in order to find a stable (hence generalizing) estimator. In this paper, we consider a spectral estimator based
https://www.researchgate.net/publication/221619956_Spectral_Regularization_for_Support_Estimation
Spectral Regularization for Support Estimation. Conference Paper (PDF Available) · January 2010 ... [22, 16, 2], and spectral methods for support estimation [9]. Therefore knowledge of the speed ...
https://papers.nips.cc/paper/4062-spectral-regularization-for-support-estimation
In particular, they are the key ingredient to prove the universal consistency of the spectral estimators and in this respect they are the analogue of universal kernels for supervised problems. Numerical experiments show that spectral estimators compare favorably to state of the art machine learning algorithms for density support estimation.
https://core.ac.uk/display/54764122
Spectral Regularization for Support Estimation. By E. De Vito, ... they are the key ingredient to prove the universal consistency of the spectral estimators and in this respect they are the analogue of universal kernels for supervised problems. ... experiments show that spectral estimators compare favorably to state of the art machine learning ...Author: E. De Vito, L. Rosasco and A. Toigo
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.207.7010
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In this paper we consider the problem of learning from data the support of a probability distribution when the distribution does not have a density (with respect to some reference measure). We propose a new class of regularized spectral estimators based on a new notion of reproducing kernel Hilbert space, which we ...
https://ieeexplore.ieee.org/document/6494328
Sparse Spatial Spectral Estimation: A Covariance Fitting Algorithm, Performance and Regularization Abstract: In this paper, the sparse spectrum fitting (SpSF) algorithm for the estimation of directions-of-arrival (DOAs) of multiple sources is introduced, and its asymptotic consistency and effective regularization under both asymptotic and ...Cited by: 74
https://www.sciencedirect.com/science/article/pii/S0167865513003619
Geometrical and computational aspects of Spectral Support Estimation for novelty detection. ... emphasizing its geometrical and computational aspects. ... A. ToigoSpectral regularization for support estimation. Advances in Neural Information Processing Systems, NIPS …Cited by: 2
https://economics.yale.edu/sites/default/files/files/Workshops-Seminars/Econometrics/florens-061004-18.pdf
Linear Inverse Problems in Structural Econometrics Estimation based on spectral decomposition and regularization⁄ Marine Carrasco University of Rochester Jean-Pierre Florens Universit´e de Toulouse (GREMAQ and IDEI) Eric Renault University of North Carolina, Chapel Hill
https://web.stanford.edu/~hastie/Papers/mazumder10a.pdf
Spectral Regularization Algorithms for Learning Large Incomplete Matrices Rahul Mazumder [email protected] Trevor Hastie∗ [email protected] Department of Statistics Stanford University Stanford, CA 94305 Robert Tibshirani† [email protected] Department of Health, Research and Policy Stanford University Stanford, CA 94305 Editor: Tommi ...
https://en.wikipedia.org/wiki/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 overfitting.Spectral regularization can be used in a broad range of applications, from deblurring images to classifying emails into a spam folder and a non-spam folder.
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