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Median Relational Generalized Learning Vector Quantization

Copyright (C) 2017-2018
Benjamin Paaßen
AG Machine Learning
Centre of Excellence Cognitive Interaction Technology (CITEC)
University of Bielefeld

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

Introduction

This is a Java 7, fully MATLAB (R)-compatible implementation of median generalized learning vector quantization (MGLVQ) for dissimilarity data as proposed by Nebel, Hammer, Frohberg, and Villmann (2015). 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). 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 GLVQ takes a matrix of pairwise distances or dissimilarities as input, which is why we call it relational.

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).

The main advantages of median relational GLVQ compared to other distance-based classifiers are

  1. Because the number of prototypes is small compared to the number of data points, only very few distances need to be computed to classify new data points.
  2. The prototypes used for classification are well-interpretable because they directly correspond to data points and thus give the option to inspect and explain the model, as well as making sense of the data.
  3. Even atypical distance measures can be treated, e.g. distances which are asymmetric and do not conform to the triangular inequality.

Installation

This implementation is written for Java 7 and depends only on the de.cit-ec.ml.rng package for initialization. It is also fully compatible with Matlab as it only interfaces with primitive data types. You can access this package by either downloading the distribution package or declaring a maven dependency to

<dependency>
	<groupId>de.cit-ec.ml</groupId>
	<artifactId>mrglvq</artifactId>
	<version>0.1.0</version>
	<scope>compile</scope>
</dependency>

If you want to compile the package from source you can download the source code via git pull https://gitlab.ub.uni-bielefeld.de/bpaassen/median_relational_glvq.git and use the command mvn package to compile a .jar distribution.

You can download the javadoc either via maven or directly as part of the distribution package or compile it yourself by downloading the source code via git pull https://gitlab.ub.uni-bielefeld.de/bpaassen/median_relational_glvq.git and using the command mvn generate-sources javadoc:javadoc.

If you want to use the package from MATLAB, please download the distribution package and add the line

javaaddpath mrglvq-0.1.0.jar;

to your MATLAB script.

Quickstart

You can obtain a classification model by using the MedianRelationalGLVQ.train() method. In particular, if you have a data set given in term of a distance matrix D and a m x 1 label vector Y, you can call:

final MedianRelationalGLVQModel model = MedianRelationalGLVQ.train(D, Y); // Java
model = de.citec.ml.mrglvq.MedianRelationalGLVQ.train(D, Y); % MATLAB

This results in a MedianRelationalGLVQModel object which can be used for further queries. In particular:

final int[] Y2 = MedianRelationalGLVQ.classify(D2, model); // Java
Y2 = de.citec.ml.mrglvq.MedianRelationalGLVQ.classify(D2, model) %MATLAB

classifies new data points given the distances D2 from the new data points to the prototypes.

Background

This is a short introduction regarding the background of median generalized learning vector quantization. For a more comprehensive explanation, I recommend to consult Nebel et al. (2015).

Learning Vector Quantization

Assume that we have data points x1, ..., xm and labels for these data points y1, ..., ym. Then, learning vector quantization (Kohonen, 1995) aims at finding K prototypes w1, ..., wK with labels z1, ..., zK such that the data points can be correctly classified by using a one-nearest neighbor rule on the prototypes. That is: If we assign to each data point the label of the closest prototype, we misclassify as few data points as possible.

This problem as such is NP-hard. However, we can approximate the problem via the generalized learning vector quantization cost function as proposed by Sato and Yamada (1995):

E = Σi=1,...,m Φ( (di+ - di-) / (di+ + di-) )

where di+ is the distance of the i-th data point to the closest prototype with the same label, di- is the distance of the i-th data point to the closest prototype with a different label, and Φ is a non-linear function, typically a sigmoid. Note that this error function measures exactly the classification error if Φ maps any input smaller than 0 to 0 and any input larger than 0 to 1, that is, if the data point is further away from the closest correct prototype compared to the closest wrong prototype, we count this as an error, and otherwise we do not.

Median Generalized Learning Vector Quantization

Median LVQ imposes the additional restriction that prototypes may not be any points in the data space but are restricted to be one of the training data points. This implies that we can not optimize the GLVQ error function via continuous schemes like stochastic gradient descent but we require a discrete optimization scheme. Nebel et al. (2015) propose an expectation maximization scheme which re-writes the GLVQ error function to a log-likelihood function as follows:

L = Σi=1,...,m log(4 - (di+ - di-) / (di+ + di-) )

Note that maximizing this likelihood is equivalent to minimizing E if Φ is the log function and the constant in front is large enough (in this case, 4). The expectation step consists essentially of re-computing the closest prototypes for each data point as well as the respective distances, while the maximization step consists of replacing one prototype with a different data point from the same Voronoi cell such that L is improved. As an initialization for the prototypes we use relational neural gas which already provides a good starting point where prototypes are representative for their respective class.

License

This documentation is licensed under the terms of the creative commons attribution-shareAlike 4.0 international (CC BY-SA 4.0) license. The code contained alongside this documentation is licensed under the GNU General Public License Version 3 license. A copy of this license is contained in the gpl-3.0.md file alongside this README.

Literature

  • Kohonen, T. (1995). Learning Vector Quantization. In: Self-Organizing Maps, 175-189. doi: 10.1007/978-3-642-97610-0_6
  • Sato, A., and Yamada, K. (1995). Generalized Learning Vector Quantization. In: Tesauro, G., Touretzky, D., and Leen, T. (eds.) Proceedings of the 7th Conference on Advances in Neural Information Processing (NIPS 1995), 423-429. Link
  • Nebel, D., Hammer, B., Frohberg, K., and Villmann, T. (2015). Median variants of learning vector quantization for learning of dissimilarity data. Neurocomputing, 169, pp. 295-305. doi: 10.1016/j.neucom.2014.12.096
  • Paaßen, B. (2018). Median Relational Generalized Learning Vector Quantization. Bielefeld University. doi: 10.4119/unibi/2916990