6,764 research outputs found
Expertsâ consensus to identify elements of career management competencies in Work-Based Learning (WBL) program using Fuzzy Delphi Analysis
This study aimed to obtain expertsâ opinion
and consensus on the elements of career management competencies that can
be developed through the Work-Based Learning (WBL) program in polytechnic
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Imaging spectrometers measure electromagnetic energy scattered in their
instantaneous field view in hundreds or thousands of spectral channels with
higher spectral resolution than multispectral cameras. Imaging spectrometers
are therefore often referred to as hyperspectral cameras (HSCs). Higher
spectral resolution enables material identification via spectroscopic analysis,
which facilitates countless applications that require identifying materials in
scenarios unsuitable for classical spectroscopic analysis. Due to low spatial
resolution of HSCs, microscopic material mixing, and multiple scattering,
spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus,
accurate estimation requires unmixing. Pixels are assumed to be mixtures of a
few materials, called endmembers. Unmixing involves estimating all or some of:
the number of endmembers, their spectral signatures, and their abundances at
each pixel. Unmixing is a challenging, ill-posed inverse problem because of
model inaccuracies, observation noise, environmental conditions, endmember
variability, and data set size. Researchers have devised and investigated many
models searching for robust, stable, tractable, and accurate unmixing
algorithms. This paper presents an overview of unmixing methods from the time
of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models
are first discussed. Signal-subspace, geometrical, statistical, sparsity-based,
and spatial-contextual unmixing algorithms are described. Mathematical problems
and potential solutions are described. Algorithm characteristics are
illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensin
Robustness and Outliers
ProducciĂłn CientĂficaUnexpected deviations from assumed models as well as the presence of certain amounts of outlying data are common in most practical statistical applications. This fact could lead to undesirable solutions when applying non-robust statistical techniques. This is often the case in cluster analysis, too. The search for homogeneous groups with large heterogeneity between them can be spoiled due to the lack of robustness of standard clustering methods. For instance, the presence of (even few) outlying observations may result in heterogeneous clusters artificially joined together or in the detection of spurious clusters merely made up of outlying observations. In this chapter we will analyze the effects of different kinds of outlying data in cluster analysis and explore several alternative methodologies designed to avoid or minimize their undesirable effects.Ministerio de EconomĂa, Industria y Competitividad (MTM2014-56235-C2-1-P)Junta de Castilla y LeĂłn (programa de apoyo a proyectos de investigaciĂłn â Ref. VA212U13
Robust constrained fuzzy clustering
It is well-known that outliers and noisy data can be very harmful when applying
clustering methods. Several fuzzy clustering methods which are able
to handle the presence of noise have been proposed. In this work, we propose
a robust clustering approach called F-TCLUST based on an âimpartialâ
(i.e., self-determined by data) trimming. The proposed approach considers
an eigenvalue ratio constraint that makes it a mathematically well-defined
problem and serves to control the allowed differences among cluster scatters.
A computationally feasible algorithm is proposed for its practical implementation.
Some guidelines about how to choose the parameters controlling the
performance of the fuzzy clustering procedure are also given.EstadĂstica e I
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