Median Generalized Learning Vector Quantization for Distance Data

Paaßen B (2018) : Bielefeld University. doi:10.4119/unibi/2916990.

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Abstract / Bemerkung
This is a Java 7, fully MATLAB (R)-compatible implementation of _median generalized learning vector quantization for dissimilarity data_ (MGLVQ) as proposed by [Nebel, Hammer, Frohberg, and Villmann (2015)](https://doi.org/10.1016/j.neucom.2014.12.096). _Learning vector quantization_ (LVQ) is a classification algorithm which represents classes in terms of _prototypes_ and classifies data by assigning each data point to the class of the closest prototype ([Kohonen, 1995](https://doi.org/10.1007/978-3-642-97610-0_6)). Median versions of LVQ use data points as prototypes, that is: Each prototype corresponds exactly to a data point from the training data. This particular implementation of median LVQ represents the data in terms of pairwise distances or dissimilarities. The input to this algorithm is a m x m distance matrix D, a number of prototypes per class K and a m x 1 vector of training data labels Y, and the output is an array of prototypes W with K prototypes per class, given as data point indices. The training is performed according to an expectation maximization scheme suggested by ([Nebel, Hammer, Frohberg, and Villmann, 2015](https://doi.org/10.1016/j.neucom.2014.12.096)).
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This Median Generalized Learning Vector Quantization for Distance Data is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/
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Paaßen B. (2018): Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University. doi:10.4119/unibi/2916990.
Paaßen, B. (2018). Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University. doi:10.4119/unibi/2916990
Paaßen, B. (2018). Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University. doi:10.4119/unibi/2916990.
Paaßen, B., 2018. Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University. doi:10.4119/unibi/2916990
B. Paaßen, Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University, 2018. doi:10.4119/unibi/2916990.
Paaßen, B.: Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University (2018). doi:10.4119/unibi/2916990.
Paaßen, Benjamin. Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University, 2018. doi:10.4119/unibi/2916990
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2018-01-27T14:07:29Z
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Zuletzt Hochgeladen
2018-01-27T14:07:29Z

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The original research paper by Nebel et al. describing median generalized learning vector quantization on dissimilarity data.
Software:
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Publically accesible GitLab page including the source files for the implementation.

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