Download de.cit-ec.ml JAR files with all dependencies
mrglvq from group de.cit-ec.ml (version 0.1.0)
This project contains a Java implementation of median relational generalized learning vector
quantization as proposed by Nebel, Hammer, Frohberg, and Villmann
(2015, doi:10.1016/j.neucom.2014.12.096). Given a matrix of pairwise distances D and a
vector of labels Y it identifies prototypical data points (i.e. rows of D) which help
to classify the data set using a simple nearest neighbor rule. In particular, the algorithm
optimizes the generalized learning vector quantization cost function (Sato and Yamada, 1995)
via an expectation maximization scheme where in each iteration one prototype 'jumps' to
another data point in order to improve the cost function. If the cost function can not be
improved anymore for any of the data points, the algorithm terminates.
Artifact mrglvq
Group de.cit-ec.ml
Version 0.1.0
Last update 27. January 2018
Organization not specified
URL https://gitlab.ub.uni-bielefeld.de/bpaassen/median_relational_glvq
License The GNU General Public License, Version 3
Dependencies amount 1
Dependencies rng,
There are maybe transitive dependencies!
Group de.cit-ec.ml
Version 0.1.0
Last update 27. January 2018
Organization not specified
URL https://gitlab.ub.uni-bielefeld.de/bpaassen/median_relational_glvq
License The GNU General Public License, Version 3
Dependencies amount 1
Dependencies rng,
There are maybe transitive dependencies!
rng from group de.cit-ec.ml (version 1.0.0)
This is an implementation of the Neural Gas algorithm on
distance data (Relational Neural Gas) for unsupervised clustering.
We recommend that you use the functions provided by the RelationalNeuralGas
class for your purposes. All other classes and functions are utilities which
are used by this central class. In particular, you can use RelationalNeuralGas.train()
to obtain a RNGModel (i.e. a clustering of your data), and subsequently
you can use RelationalNeuralGas.getAssignments() to obtain the resulting
cluster assignments, and RelationalNeuralGas.classify() to cluster new points
which are not part of the training data set.
The underlying scientific work is summarized nicely in the dissertation
"Topographic Mapping of Dissimilarity Datasets" by Alexander Hasenfuss
(2009).
The basic properties of an Relational Neural Gas algorithm are the following:
1.) It is relational: The data is represented only in terms of a pairwise
distance matrix.
2.) It is a clustering method: The algorithm provides a clustering model,
that is: After calculation,
each data point should be assigned to a cluster (for this package here we
only consider hard clustering, that is: each data point is assigned to
exactly one cluster).
3.) It is a vector quantization method: Each cluster corresponds to a
prototype, which is in the center of the
cluster and data points are assigned to the cluster if and only if they are
closest to this particular prototype.
4.) It is rank-based: The updates of the prototypes depend only on
the distance ranking, not on the absolute value of the distances.
Artifact rng
Group de.cit-ec.ml
Version 1.0.0
Last update 26. January 2018
Organization not specified
URL https://gitlab.ub.uni-bielefeld.de/bpaassen/relational_neural_gas
License The GNU General Public License, Version 3
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group de.cit-ec.ml
Version 1.0.0
Last update 26. January 2018
Organization not specified
URL https://gitlab.ub.uni-bielefeld.de/bpaassen/relational_neural_gas
License The GNU General Public License, Version 3
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
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