12,702 research outputs found
Image patch analysis of sunspots and active regions. II. Clustering via matrix factorization
Separating active regions that are quiet from potentially eruptive ones is a
key issue in Space Weather applications. Traditional classification schemes
such as Mount Wilson and McIntosh have been effective in relating an active
region large scale magnetic configuration to its ability to produce eruptive
events. However, their qualitative nature prevents systematic studies of an
active region's evolution for example. We introduce a new clustering of active
regions that is based on the local geometry observed in Line of Sight
magnetogram and continuum images. We use a reduced-dimension representation of
an active region that is obtained by factoring the corresponding data matrix
comprised of local image patches. Two factorizations can be compared via the
definition of appropriate metrics on the resulting factors. The distances
obtained from these metrics are then used to cluster the active regions. We
find that these metrics result in natural clusterings of active regions. The
clusterings are related to large scale descriptors of an active region such as
its size, its local magnetic field distribution, and its complexity as measured
by the Mount Wilson classification scheme. We also find that including data
focused on the neutral line of an active region can result in an increased
correspondence between our clustering results and other active region
descriptors such as the Mount Wilson classifications and the value. We
provide some recommendations for which metrics, matrix factorization
techniques, and regions of interest to use to study active regions.Comment: Accepted for publication in the Journal of Space Weather and Space
Climate (SWSC). 33 pages, 12 figure
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
Multi-view graph embedding has become a widely studied problem in the area of
graph learning. Most of the existing works on multi-view graph embedding aim to
find a shared common node embedding across all the views of the graph by
combining the different views in a specific way. Hub detection, as another
essential topic in graph mining has also drawn extensive attentions in recent
years, especially in the context of brain network analysis. Both the graph
embedding and hub detection relate to the node clustering structure of graphs.
The multi-view graph embedding usually implies the node clustering structure of
the graph based on the multiple views, while the hubs are the boundary-spanning
nodes across different node clusters in the graph and thus may potentially
influence the clustering structure of the graph. However, none of the existing
works in multi-view graph embedding considered the hubs when learning the
multi-view embeddings. In this paper, we propose to incorporate the hub
detection task into the multi-view graph embedding framework so that the two
tasks could benefit each other. Specifically, we propose an auto-weighted
framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain
network analysis. The MVGE-HD framework learns a unified graph embedding across
all the views while reducing the potential influence of the hubs on blurring
the boundaries between node clusters in the graph, thus leading to a clear and
discriminative node clustering structure for the graph. We apply MVGE-HD on two
real multi-view brain network datasets (i.e., HIV and Bipolar). The
experimental results demonstrate the superior performance of the proposed
framework in brain network analysis for clinical investigation and application
Characterization of complex networks: A survey of measurements
Each complex network (or class of networks) presents specific topological
features which characterize its connectivity and highly influence the dynamics
of processes executed on the network. The analysis, discrimination, and
synthesis of complex networks therefore rely on the use of measurements capable
of expressing the most relevant topological features. This article presents a
survey of such measurements. It includes general considerations about complex
network characterization, a brief review of the principal models, and the
presentation of the main existing measurements. Important related issues
covered in this work comprise the representation of the evolution of complex
networks in terms of trajectories in several measurement spaces, the analysis
of the correlations between some of the most traditional measurements,
perturbation analysis, as well as the use of multivariate statistics for
feature selection and network classification. Depending on the network and the
analysis task one has in mind, a specific set of features may be chosen. It is
hoped that the present survey will help the proper application and
interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of
measurements for inclusion are welcomed by the author
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