28,423 research outputs found
Post-processing partitions to identify domains of modularity optimization
We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP)
algorithm to prune and prioritize different network community structures
identified across multiple runs of possibly various computational heuristics.
Given a set of partitions, CHAMP identifies the domain of modularity
optimization for each partition ---i.e., the parameter-space domain where it
has the largest modularity relative to the input set---discarding partitions
with empty domains to obtain the subset of partitions that are "admissible"
candidate community structures that remain potentially optimal over indicated
parameter domains. Importantly, CHAMP can be used for multi-dimensional
parameter spaces, such as those for multilayer networks where one includes a
resolution parameter and interlayer coupling. Using the results from CHAMP, a
user can more appropriately select robust community structures by observing the
sizes of domains of optimization and the pairwise comparisons between
partitions in the admissible subset. We demonstrate the utility of CHAMP with
several example networks. In these examples, CHAMP focuses attention onto
pruned subsets of admissible partitions that are 20-to-1785 times smaller than
the sets of unique partitions obtained by community detection heuristics that
were input into CHAMP.Comment: http://www.mdpi.com/1999-4893/10/3/9
Multi-View Multiple Clusterings using Deep Matrix Factorization
Multi-view clustering aims at integrating complementary information from
multiple heterogeneous views to improve clustering results. Existing multi-view
clustering solutions can only output a single clustering of the data. Due to
their multiplicity, multi-view data, can have different groupings that are
reasonable and interesting from different perspectives. However, how to find
multiple, meaningful, and diverse clustering results from multi-view data is
still a rarely studied and challenging topic in multi-view clustering and
multiple clusterings. In this paper, we introduce a deep matrix factorization
based solution (DMClusts) to discover multiple clusterings. DMClusts gradually
factorizes multi-view data matrices into representational subspaces
layer-by-layer and generates one clustering in each layer. To enforce the
diversity between generated clusterings, it minimizes a new redundancy
quantification term derived from the proximity between samples in these
subspaces. We further introduce an iterative optimization procedure to
simultaneously seek multiple clusterings with quality and diversity.
Experimental results on benchmark datasets confirm that DMClusts outperforms
state-of-the-art multiple clustering solutions
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
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