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- W2802172308 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." @default.
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- W2802172308 date "2018-01-01" @default.
- W2802172308 modified "2023-10-16" @default.
- W2802172308 title "Direct Incorporation of $$L_1$$ -Regularization into Generalized Matrix Learning Vector Quantization" @default.
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- W2802172308 doi "https://doi.org/10.1007/978-3-319-91253-0_61" @default.
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