10,378 research outputs found
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Incomplete graphical model inference via latent tree aggregation
Graphical network inference is used in many fields such as genomics or
ecology to infer the conditional independence structure between variables, from
measurements of gene expression or species abundances for instance. In many
practical cases, not all variables involved in the network have been observed,
and the samples are actually drawn from a distribution where some variables
have been marginalized out. This challenges the sparsity assumption commonly
made in graphical model inference, since marginalization yields locally dense
structures, even when the original network is sparse. We present a procedure
for inferring Gaussian graphical models when some variables are unobserved,
that accounts both for the influence of missing variables and the low density
of the original network. Our model is based on the aggregation of spanning
trees, and the estimation procedure on the Expectation-Maximization algorithm.
We treat the graph structure and the unobserved nodes as missing variables and
compute posterior probabilities of edge appearance. To provide a complete
methodology, we also propose several model selection criteria to estimate the
number of missing nodes. A simulation study and an illustration flow cytometry
data reveal that our method has favorable edge detection properties compared to
existing graph inference techniques. The methods are implemented in an R
package
EC3: Combining Clustering and Classification for Ensemble Learning
Classification and clustering algorithms have been proved to be successful
individually in different contexts. Both of them have their own advantages and
limitations. For instance, although classification algorithms are more powerful
than clustering methods in predicting class labels of objects, they do not
perform well when there is a lack of sufficient manually labeled reliable data.
On the other hand, although clustering algorithms do not produce label
information for objects, they provide supplementary constraints (e.g., if two
objects are clustered together, it is more likely that the same label is
assigned to both of them) that one can leverage for label prediction of a set
of unknown objects. Therefore, systematic utilization of both these types of
algorithms together can lead to better prediction performance. In this paper,
We propose a novel algorithm, called EC3 that merges classification and
clustering together in order to support both binary and multi-class
classification. EC3 is based on a principled combination of multiple
classification and multiple clustering methods using an optimization function.
We theoretically show the convexity and optimality of the problem and solve it
by block coordinate descent method. We additionally propose iEC3, a variant of
EC3 that handles imbalanced training data. We perform an extensive experimental
analysis by comparing EC3 and iEC3 with 14 baseline methods (7 well-known
standalone classifiers, 5 ensemble classifiers, and 2 existing methods that
merge classification and clustering) on 13 standard benchmark datasets. We show
that our methods outperform other baselines for every single dataset, achieving
at most 10% higher AUC. Moreover our methods are faster (1.21 times faster than
the best baseline), more resilient to noise and class imbalance than the best
baseline method.Comment: 14 pages, 7 figures, 11 table
Multilevel comparison of large urban systems
For the first time the systems of cities in seven countries or regions among
the largest in the world (China, India, Brazil, Europe, the Former Soviet Union
(FSU), the United States and South Africa) are made comparable through the
building of spatio-temporal standardised statistical databases. We first
explain the concept of a generic evolutionary urban unit ("city") and its
necessary adaptations to the information provided by each national statistical
system. Second, the hierarchical structure and the urban growth process are
compared at macro-scale for the seven countries with reference to Zipf's and
Gibrat's model: in agreement with an evolutionary theory of urban systems,
large similarities shape the hierarchical structure and growth processes in
BRICS countries as well as in Europe and United States, despite their positions
at different stages in the urban transition that explain some structural
peculiarities. Third, the individual trajectories of some 10,000 cities are
mapped at micro-scale following a cluster analysis of their evolution over the
last fifty years. A few common principles extracted from the evolutionary
theory of urban systems can explain the diversity of these trajectories,
including a specific pattern in their geographical repartition in the Chinese
case. We conclude that the observations at macro-level when summarized as
stylised facts can help in designing simulation models of urban systems whereas
the urban trajectories identified at micro-level are consistent enough for
constituting the basis of plausible future population projections.Comment: 14 pages, 9 figures; Pumain, Denise, et al. "Multilevel comparison of
large urban systems." Cybergeo: European Journal of Geography (2015
Semantic Cross-View Matching
Matching cross-view images is challenging because the appearance and
viewpoints are significantly different. While low-level features based on
gradient orientations or filter responses can drastically vary with such
changes in viewpoint, semantic information of images however shows an invariant
characteristic in this respect. Consequently, semantically labeled regions can
be used for performing cross-view matching. In this paper, we therefore explore
this idea and propose an automatic method for detecting and representing the
semantic information of an RGB image with the goal of performing cross-view
matching with a (non-RGB) geographic information system (GIS). A segmented
image forms the input to our system with segments assigned to semantic concepts
such as traffic signs, lakes, roads, foliage, etc. We design a descriptor to
robustly capture both, the presence of semantic concepts and the spatial layout
of those segments. Pairwise distances between the descriptors extracted from
the GIS map and the query image are then used to generate a shortlist of the
most promising locations with similar semantic concepts in a consistent spatial
layout. An experimental evaluation with challenging query images and a large
urban area shows promising results
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