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Fundamentals of multirate graph signal processing
2015 49th Asilomar Conference on Signals, Systems and Computers, 2015In this work, the fundamental blocks of multirate signal processing on graphs are analyzed. First the decimator is defined, and expander is solved accordingly. Then, noble identities and lazy filter bank for graph signals are constructed. After decimation, the length of the signal changes and the original adjacency matrix is not applicable.
Teke, Oguzhan, Vaidyanathan, P. P.
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2022
In this PhD thesis, several signal-processing models applied on graphs have been studied. Half-quadratic (HQ) optimization, robust estimators, such as marginal median and M-estimators, l21 norm regularization, block least mean squares have been the fundamental basis of the proposed approaches.
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In this PhD thesis, several signal-processing models applied on graphs have been studied. Half-quadratic (HQ) optimization, robust estimators, such as marginal median and M-estimators, l21 norm regularization, block least mean squares have been the fundamental basis of the proposed approaches.
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Graph Filters for Signal Processing and Machine Learning on Graphs
IEEE Transactions on Signal ProcessingFilters 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. Increasingly, modern data also reside on networks and other irregular domains whose structure is better ...
Fernando Gama +2 more
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Discrete Signal Processing on Graphs: Graph Fourier Transform
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013We propose a novel discrete signal processing framework for the representation and analysis of datasets with complex structure. Such datasets arise in many social, economic, biological, and physical networks. Our framework extends traditional discrete signal processing theory to structured datasets by viewing them as signals represented by graphs, so ...
José M. F. Moura, Aliaksei Sandryhaila
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Signal processing on graphs: Performance of graph structure estimation
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016A class of models for describing sets of time series generated by interacting agents using directed, weighted graphs is introduced. A computationally tractable algorithm for estimating the graph adjacency matrix of this model from observed time series data is presented.
Jonathan Mei, José M. F. Moura
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Simulation of signal flow graphs for signal processing systems
ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005There are many interacting issues which must be investigated fully before a multiprocessor system for signal processing applications is integrated. Among the most important of these issues are the system architecture, system configuration, and the implementation technology.
T. A. Lanfear +2 more
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Robust Graph Signal Processing in the Presence of Uncertainties on Graph Topology
2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018In this paper we address the problem of analyzing signals defined over graphs whose topology is known only with some uncertainty about the presence/absence of some edges. This situation arises in all cases where edges are associated to a set of elements (vertices), but the association rule may be affected by errors.
Ceci E., Barbarossa S.
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Distributed signal processing with graph spectral dictionaries
2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015We study the distributed processing of graph signals that are well represented by graph spectral dictionaries. We first analyze the impact of quantization noise in the distributed computation of polynomial dictionary operators that are commonly used in various signal processing tasks.
Thanou, Dorina, Frossard, Pascal
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Introduction to Graph Signal Processing
2022An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph ...
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Graph Signal Processing and Applications: A Survey
2022 IEEE International Conference on Consumer Electronics (ICCE), 2022William C. da Rosa +3 more
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