Zobrazeno 1 - 10
of 78
pro vyhledávání: '"Naumova, Valeriya"'
Kernel ridge regression (KRR) is a popular scheme for non-linear non-parametric learning. However, existing implementations of KRR require that all the data is stored in the main memory, which severely limits the use of KRR in contexts where data siz
Externí odkaz:
http://arxiv.org/abs/2108.10411
We introduce a construction of multiscale tight frames on general domains. The frame elements are obtained by spectral filtering of the integral operator associated with a reproducing kernel. Our construction extends classical wavelets as well as gen
Externí odkaz:
http://arxiv.org/abs/2006.09870
In this work we consider numerical efficiency and convergence rates for solvers of non-convex multi-penalty formulations when reconstructing sparse signals from noisy linear measurements. We extend an existing approach, based on reduction to an augme
Externí odkaz:
http://arxiv.org/abs/1908.02503
In this paper we propose and study a family of continuous wavelets on general domains, and a corresponding stochastic discretization that we call Monte Carlo wavelets. First, using tools from the theory of reproducing kernel Hilbert spaces and associ
Externí odkaz:
http://arxiv.org/abs/1903.06594
Publikováno v:
Information and Inference: A Journal of the IMA (2020)
Single index model is a powerful yet simple model, widely used in statistics, machine learning, and other scientific fields. It models the regression function as $g()$, where a is an unknown index vector and x are the features. This paper deals
Externí odkaz:
http://arxiv.org/abs/1902.09024
We consider the problem of recovering an unknown effectively $(s_1,s_2)$-sparse low-rank-$R$ matrix $X$ with possibly non-orthogonal rank-$1$ decomposition from incomplete and inaccurate linear measurements of the form $y = \mathcal A (X) + \eta$, wh
Externí odkaz:
http://arxiv.org/abs/1801.06240
Publikováno v:
In Applied Mathematics and Computation 1 August 2022 426
For many algorithms, parameter tuning remains a challenging and critical task, which becomes tedious and infeasible in a multi-parameter setting. Multi-penalty regularization, successfully used for solving undetermined sparse regression of problems o
Externí odkaz:
http://arxiv.org/abs/1710.03971
Autor:
Naumova, Valeriya, Schnass, Karin
This paper extends the recently proposed and theoretically justified iterative thresholding and $K$ residual means algorithm ITKrM to learning dicionaries from incomplete/masked training data (ITKrMM). It further adapts the algorithm to the presence
Externí odkaz:
http://arxiv.org/abs/1701.03655