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Erschienen in:
Three-Dimensional Model of Signal Processing in the Presynaptic Bouton of the Neuron Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Direct Incorporation of \(L_1\)-Regularization into Generalized Matrix Learning Vector Quantization

verfasst von : Falko Lischke, Thomas Neumann, Sven Hellbach, Thomas Villmann, Hans-Joachim Böhme

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

Frequently, high-dimensional features are used to represent data to be classified. This paper proposes a new approach to learn interpretable classification models from such high-dimensional data representation. To this end, we extend a popular prototype-based classification algorithm, the matrix learning vector quantization, to incorporate an enhanced feature selection objective via \(L_1\)-regularization. In contrast to previous work, we propose a framework that directly optimizes this objective using the alternating direction method of multipliers (ADMM) and manifold optimization. We evaluate our method on synthetic data and on real data for speech-based emotion recognition. Particularly, we show that our method achieves state-of-the-art results on the Berlin Database of Emotional speech and show its abilities to select relevant dimensions from the eGeMAPS set of audio features.

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Vorheriges Kapitel Multi-level Aggregation in Face Recognition
Nächstes Kapitel Classifiers for Matrix Normal Images: Derivation and Testing
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Metadaten
Titel
Direct Incorporation of -Regularization into Generalized Matrix Learning Vector Quantization
verfasst von
Falko Lischke
Thomas Neumann
Sven Hellbach
Thomas Villmann
Hans-Joachim Böhme
Copyright-Jahr
2018
Buch
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-91252-3

Electronic ISBN: 978-3-319-91253-0

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-91253-0_61