2018
DOI: 10.48550/arxiv.1801.02303
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Joint Estimation of Low-Rank Components and Connectivity Graph in High-Dimensional Graph Signals: Application to Brain Imaging

Abstract: This paper presents a graph signal processing algorithm to uncover the intrinsic low-rank components and the underlying graph of a high-dimensional, graph-smooth and grosslycorrupted dataset. In our problem formulation, we assume that the perturbation on the low-rank components is sparse and the signal is smooth on the graph. We propose an algorithm to estimate the low-rank components with the help of the graph and refine the graph with better estimated low-rank components. We propose to perform the low-rank e… Show more

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Cited by 1 publication
(2 citation statements)
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References 48 publications
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“…For example, [27] introduced a graph regression model for learning brain structural connectivity of patients with Alzheimer's disease, which is based on the signal smoothness model discussed in Section III-A. A similar framework [73],…”
Section: B Brain Signal Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…For example, [27] introduced a graph regression model for learning brain structural connectivity of patients with Alzheimer's disease, which is based on the signal smoothness model discussed in Section III-A. A similar framework [73],…”
Section: B Brain Signal Analysismentioning
confidence: 99%
“…Instead of the smoothness model adopted in [27], [73], the authors in [54] have utilized models on causal dependencies and proposed to infer effective connectivity networks of brain regions that may shed light on the understanding of the cause behind epilepsy. The signals that they use are electrocorticography (ECoG) time series data before and after ictal onset of seizures of epilepsy.…”
Section: B Brain Signal Analysismentioning
confidence: 99%