Median Generalized Learning Vector Quantization for Distance Data

Paaßen B (2018)
Bielefeld University.

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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)).
Stichworte
classification; learning vector quantization; distance-based machine learning
Erscheinungsjahr
2018
Page URI
https://pub.uni-bielefeld.de/record/2916990

Zitieren

Paaßen B. Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University; 2018.
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.
Paaßen, B., 2018. Median Generalized Learning Vector Quantization for Distance Data, Bielefeld University.
B. Paaßen, Median Generalized Learning Vector Quantization for Distance Data, Bielefeld University, 2018.
Paaßen, B.: Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University (2018).
Paaßen, Benjamin. Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University, 2018.
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Zuletzt Hochgeladen
2019-09-25T06:53:16Z
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29de13a2285d9f9b89d4c44ae8c0b9eb
Name
Access Level
OA Open Access
Zuletzt Hochgeladen
2019-09-25T06:53:16Z
MD5 Prüfsumme
29de13a2285d9f9b89d4c44ae8c0b9eb

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

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