13,003 research outputs found
Parsimonious Shifted Asymmetric Laplace Mixtures
A family of parsimonious shifted asymmetric Laplace mixture models is
introduced. We extend the mixture of factor analyzers model to the shifted
asymmetric Laplace distribution. Imposing constraints on the constitute parts
of the resulting decomposed component scale matrices leads to a family of
parsimonious models. An explicit two-stage parameter estimation procedure is
described, and the Bayesian information criterion and the integrated completed
likelihood are compared for model selection. This novel family of models is
applied to real data, where it is compared to its Gaussian analogue within
clustering and classification paradigms
Solving the flow fields in conduits and networks using energy minimization principle with simulated annealing
In this paper, we propose and test an intuitive assumption that the pressure
field in single conduits and networks of interconnected conduits adjusts itself
to minimize the total energy consumption required for transporting a specific
quantity of fluid. We test this assumption by using linear flow models of
Newtonian fluids transported through rigid tubes and networks in conjunction
with a simulated annealing (SA) protocol to minimize the total energy cost. All
the results confirm our hypothesis as the SA algorithm produces very close
results to those obtained from the traditional deterministic methods of
identifying the flow fields by solving a set of simultaneous equations based on
the conservation principles. The same results apply to electric ohmic
conductors and networks of interconnected ohmic conductors. Computational
experiments conducted in this regard confirm this extension. Further studies
are required to test the energy minimization hypothesis for the non-linear flow
systems.Comment: 11 pages, 2 figures, 1 tabl
How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?
In numerous applicative contexts, data are too rich and too complex to be
represented by numerical vectors. A general approach to extend machine learning
and data mining techniques to such data is to really on a dissimilarity or on a
kernel that measures how different or similar two objects are. This approach
has been used to define several variants of the Self Organizing Map (SOM). This
paper reviews those variants in using a common set of notations in order to
outline differences and similarities between them. It discusses the advantages
and drawbacks of the variants, as well as the actual relevance of the
dissimilarity/kernel SOM for practical applications
Mixtures of Shifted Asymmetric Laplace Distributions
A mixture of shifted asymmetric Laplace distributions is introduced and used
for clustering and classification. A variant of the EM algorithm is developed
for parameter estimation by exploiting the relationship with the general
inverse Gaussian distribution. This approach is mathematically elegant and
relatively computationally straightforward. Our novel mixture modelling
approach is demonstrated on both simulated and real data to illustrate
clustering and classification applications. In these analyses, our mixture of
shifted asymmetric Laplace distributions performs favourably when compared to
the popular Gaussian approach. This work, which marks an important step in the
non-Gaussian model-based clustering and classification direction, concludes
with discussion as well as suggestions for future work
Review of Metaheuristics and Generalized Evolutionary Walk Algorithm
Metaheuristic algorithms are often nature-inspired, and they are becoming
very powerful in solving global optimization problems. More than a dozen of
major metaheuristic algorithms have been developed over the last three decades,
and there exist even more variants and hybrid of metaheuristics. This paper
intends to provide an overview of nature-inspired metaheuristic algorithms,
from a brief history to their applications. We try to analyze the main
components of these algorithms and how and why they works. Then, we intend to
provide a unified view of metaheuristics by proposing a generalized
evolutionary walk algorithm (GEWA). Finally, we discuss some of the important
open questions.Comment: 14 page
Optimizing an Organized Modularity Measure for Topographic Graph Clustering: a Deterministic Annealing Approach
This paper proposes an organized generalization of Newman and Girvan's
modularity measure for graph clustering. Optimized via a deterministic
annealing scheme, this measure produces topologically ordered graph clusterings
that lead to faithful and readable graph representations based on clustering
induced graphs. Topographic graph clustering provides an alternative to more
classical solutions in which a standard graph clustering method is applied to
build a simpler graph that is then represented with a graph layout algorithm. A
comparative study on four real world graphs ranging from 34 to 1 133 vertices
shows the interest of the proposed approach with respect to classical solutions
and to self-organizing maps for graphs
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