Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Volkmer, Toni"'
We consider fast, provably accurate algorithms for approximating functions on the $d$-dimensional torus, $f: \mathbb{ T }^d \rightarrow \mathbb{C}$, that are sparse (or compressible) in the Fourier basis. In particular, suppose that the Fourier coeff
Externí odkaz:
http://arxiv.org/abs/2012.09889
Publikováno v:
SIAM Journal on Mathematics of Data Science, 3(2), p.758-785, 2021
We generalize a graph-based multiclass semi-supervised classification technique based on diffuse interface methods to multilayer graphs. Besides the treatment of various applications with an inherent multilayer structure, we present a very flexible a
Externí odkaz:
http://arxiv.org/abs/2007.05239
In this paper a sublinear time algorithm is presented for the reconstruction of functions that can be represented by just few out of a potentially large candidate set of Fourier basis functions in high spatial dimensions, a so-called high-dimensional
Externí odkaz:
http://arxiv.org/abs/2006.13053
In this paper we present the first known deterministic algorithm for the construction of multiple rank-1 lattices for the approximation of periodic functions of many variables. The algorithm works by converting a potentially large reconstructing sing
Externí odkaz:
http://arxiv.org/abs/2003.09753
We construct a least squares approximation method for the recovery of complex-valued functions from a reproducing kernel Hilbert space on $D \subset \mathbb{R}^d$. The nodes are drawn at random for the whole class of functions and the error is measur
Externí odkaz:
http://arxiv.org/abs/1911.10111
In this paper, we develop a sublinear-time compressive sensing algorithm for approximating functions of many variables which are compressible in a given Bounded Orthonormal Product Basis (BOPB). The resulting algorithm is shown to both have an associ
Externí odkaz:
http://arxiv.org/abs/1909.09564
The graph Laplacian is a standard tool in data science, machine learning, and image processing. The corresponding matrix inherits the complex structure of the underlying network and is in certain applications densely populated. This makes computation
Externí odkaz:
http://arxiv.org/abs/1808.04580
Autor:
Kämmerer, Lutz, Volkmer, Toni
In this work, we consider the approximate reconstruction of high-dimensional periodic functions based on sampling values. As sampling schemes, we utilize so-called reconstructing multiple rank-1 lattices, which combine several preferable properties s
Externí odkaz:
http://arxiv.org/abs/1802.06639
The reconstruction of high-dimensional sparse signals is a challenging task in a wide range of applications. In order to deal with high-dimensional problems, efficient sparse fast Fourier transform algorithms are essential tools. The second and third
Externí odkaz:
http://arxiv.org/abs/1711.05152
Publikováno v:
In Applied and Computational Harmonic Analysis March 2021 51:225-257
