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A flagship conference of the IEEE Signal Processing Society, GlobalSIP is structured around coherent symposia that explore new and emerging developments in the field, while maintaining a format that encourages accessibility to interested researchers and fosters interaction and cross-pollination of ideas.

Sampling and reconstruction of bandlimited graph signals have well-appreciated merits for dimensionality reduction, affordable storage, and online processing of streaming network data. However, these parsimonious signals are oftentimes encountered with high-dimensional linear inverse problems. Hence, interest shifts from reconstructing the signal itself towards instead approximating the input to a prescribed linear operator efficiently.

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5 Views

Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. New challenges arise in multilayer graph clustering for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a theoretical framework for multilayer spectral graph clustering of the nodes via convex layer aggregation.

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We consider functions on a weighted combinatorial graph G (finite or countable) whose evolution in time −∞ < t < ∞ is governed by the Schro ̈dinger type equation ∂g(t, v)/∂t = i∆g(t, v), v ∈ V (G), with the combinatorial Laplace operator on the right side.

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1 Views

This paper presents a new methodology to detect low-frequency
oscillations in power grids by use of time-synchronized data
from phasor measurement unit (PMU). Principal component analysis
(PCA) is first applied to the massive PMU data to extract the
low-dimensional features, i.e., the principal components (PCs). Then
based on persistent homology, a \emph{cyclicity response function}
is proposed to detect low-frequency oscillations through the use of
PCs. Whenever the cyclicity response exceeds a numerically robust

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8 Views

Sampling and reconstruction of bandlimited graph signals have well-appreciated merits for dimensionality reduction, affordable storage, and online processing of streaming network data. However, these parsimonious signals are oftentimes encountered with high-dimensional linear inverse problems. Hence, interest shifts from reconstructing the signal itself towards instead approximating the input to a prescribed linear operator efficiently.

Categories:
9 Views

In this paper, a new active learning scheme is proposed for linear
regression problems with the objective of resolving the insufficient
training data problem and the unreliable training data labeling prob-
lem. A pool-based active regression technique is applied to select the
optimal training data to label from the overall data pool. Then, com-
pressive sensing is exploited to remove labeling errors if the errors
are sparse and have large enough magnitudes, which are called large

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7 Views

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