20,764 research outputs found
Reconstructing the world trade multiplex: the role of intensive and extensive biases
In economic and financial networks, the strength of each node has always an
important economic meaning, such as the size of supply and demand, import and
export, or financial exposure. Constructing null models of networks matching
the observed strengths of all nodes is crucial in order to either detect
interesting deviations of an empirical network from economically meaningful
benchmarks or reconstruct the most likely structure of an economic network when
the latter is unknown. However, several studies have proved that real economic
networks and multiplexes are topologically very different from configurations
inferred only from node strengths. Here we provide a detailed analysis of the
World Trade Multiplex by comparing it to an enhanced null model that
simultaneously reproduces the strength and the degree of each node. We study
several temporal snapshots and almost one hundred layers (commodity classes) of
the multiplex and find that the observed properties are systematically well
reproduced by our model. Our formalism allows us to introduce the (static)
concept of extensive and intensive bias, defined as a measurable tendency of
the network to prefer either the formation of extra links or the reinforcement
of link weights, with respect to a reference case where only strengths are
enforced. Our findings complement the existing economic literature on (dynamic)
intensive and extensive trade margins. More in general, they show that
real-world multiplexes can be strongly shaped by layer-specific local
constraints
Matching Image Sets via Adaptive Multi Convex Hull
Traditional nearest points methods use all the samples in an image set to
construct a single convex or affine hull model for classification. However,
strong artificial features and noisy data may be generated from combinations of
training samples when significant intra-class variations and/or noise occur in
the image set. Existing multi-model approaches extract local models by
clustering each image set individually only once, with fixed clusters used for
matching with various image sets. This may not be optimal for discrimination,
as undesirable environmental conditions (eg. illumination and pose variations)
may result in the two closest clusters representing different characteristics
of an object (eg. frontal face being compared to non-frontal face). To address
the above problem, we propose a novel approach to enhance nearest points based
methods by integrating affine/convex hull classification with an adapted
multi-model approach. We first extract multiple local convex hulls from a query
image set via maximum margin clustering to diminish the artificial variations
and constrain the noise in local convex hulls. We then propose adaptive
reference clustering (ARC) to constrain the clustering of each gallery image
set by forcing the clusters to have resemblance to the clusters in the query
image set. By applying ARC, noisy clusters in the query set can be discarded.
Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method
outperforms single model approaches and other recent techniques, such as Sparse
Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant
Analysis.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
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