Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Shuman, David I."'
Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph. Graphs are versatile, able to model irregular interactions, easy to interpret, and
Externí odkaz:
http://arxiv.org/abs/2303.12211
Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Inc
Externí odkaz:
http://arxiv.org/abs/2211.08854
Publikováno v:
J Fourier Anal Appl 27, 70 (2021)
Ranked data sets, where m judges/voters specify a preference ranking of n objects/candidates, are increasingly prevalent in contexts such as political elections, computer vision, recommender systems, and bioinformatics. The vote counts for each ranki
Externí odkaz:
http://arxiv.org/abs/2103.04150
Autor:
Shuman, David I
Representing data residing on a graph as a linear combination of building block signals can enable efficient and insightful visual or statistical analysis of the data, and such representations prove useful as regularizers in signal processing and mac
Externí odkaz:
http://arxiv.org/abs/2006.11220
We propose and investigate two new methods to approximate $f({\bf A}){\bf b}$ for large, sparse, Hermitian matrices ${\bf A}$. The main idea behind both methods is to first estimate the spectral density of ${\bf A}$, and then find polynomials of a fi
Externí odkaz:
http://arxiv.org/abs/1808.09506
We investigate a scalable $M$-channel critically sampled filter bank for graph signals, where each of the $M$ filters is supported on a different subband of the graph Laplacian spectrum. For analysis, the graph signal is filtered on each subband and
Externí odkaz:
http://arxiv.org/abs/1608.03171
In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties -- the ability to adapt to specific signal data and a fast implementation of the dictionary. To sparsely represent signals residing
Externí odkaz:
http://arxiv.org/abs/1401.0887
We consider the problem of designing spectral graph filters for the construction of dictionaries of atoms that can be used to efficiently represent signals residing on weighted graphs. While the filters used in previous spectral graph wavelet constru
Externí odkaz:
http://arxiv.org/abs/1311.0897
Multiscale transforms designed to process analog and discrete-time signals and images cannot be directly applied to analyze high-dimensional data residing on the vertices of a weighted graph, as they do not capture the intrinsic geometric structure o
Externí odkaz:
http://arxiv.org/abs/1308.4942
One of the key challenges in the area of signal processing on graphs is to design dictionaries and transform methods to identify and exploit structure in signals on weighted graphs. To do so, we need to account for the intrinsic geometric structure o
Externí odkaz:
http://arxiv.org/abs/1307.5708