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(15 citation statements)

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“…Estimation of the brain connectivity graph using a Gaussian noise model has been proposed in [11]. On the other hand, focusing on low-rank estimation, some works have proposed to incorporate spectral graph regularization [12,13,14]. Their graphs are fixed in their algorithms, pre-computed from the noisy input data.…”

confidence: 99%

“…Estimation of the brain connectivity graph using a Gaussian noise model has been proposed in [11]. On the other hand, focusing on low-rank estimation, some works have proposed to incorporate spectral graph regularization [12,13,14]. Their graphs are fixed in their algorithms, pre-computed from the noisy input data.…”

confidence: 99%

“…• Φ f given L, M: On the other hand, for a given estimate of the low-rank data L, tr(L T Φ f L) guides the refinement of Φ f and hence the underlying connectivity graph G. In particular, a graph G that is consistent with the signal variation in L is favored: large Wi,j if row i and j of L have similar values. In many problems, the given graph for a problem can be noisy (e.g., the graph is estimated from the noisy data itself [13,14]). The proposed formulation iteratively improves Φ f using the refined low-rank data.…”

confidence: 99%

“…It is quite obvious that we need a de-noising setup for real world data. In the following therefore we motivate and discuss the second tool in depth which was introduced in [1]. The compression framework is presented in [3] and will not be repeated here for brevity.…”

confidence: 99%

“…"This techncial report is a detailed explanation of the novel low-rank recovery concepts introduced in the previous work, Fast Robust PCA on Graphs [1]. The readers can refer to [1] for experiments which are not repeated here for brevity. ".…”

confidence: 99%