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Unimodal regularisation based on beta distribution for deep ordinal regression

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Author
Vargas, Víctor Manuel
Gutiérrez, Pedro A.
Hervás-Martínez, César
Publisher
Elsevier
Date
2022
Subject
Ordinal regression
Unimodal distribution
Convolutional network
Beta distribution
Stick-breaking
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Abstract
Currently, the use of deep learning for solving ordinal classification problems, where categories follow a natural order, has not received much attention. In this paper, we propose an unimodal regularisation based on the beta distribution applied to the cross-entropy loss. This regularisation encourages the distribution of the labels to be a soft unimodal distribution, more appropriate for ordinal problems. Given that the beta distribution has two parameters that must be adjusted, a method to automatically determine them is proposed. The regularised loss function is used to train a deep neural network model with an ordinal scheme in the output layer. The results obtained are statistically analysed and show that the combination of these methods increases the performance in ordinal problems. Moreover, the proposed beta distribution performs better than other distributions proposed in previous works, achieving also a reduced computational cost.
URI
http://hdl.handle.net/10396/22071
Fuente
Pattern Recognition 122, 108310 (2022)
Versión del Editor
https://doi.org/10.1016/j.patcog.2021.108310
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