Abstract
Label ambiguity in data is one of the serious challenges in Machine Learning. In such cases, the significance of each label matters. Label Distribution Learning (LDL) is a new way to deal with label ambiguity. In LDL, instances are defined through the degree to which it is represented by its labels or classes. In this paper, deep learning using autoencoders has been proposed for a computing this degree of labels. The autoencoder consists two parts i.e., encoder and decoder. The encoder compresses the input whereas the decoder decompresses it. The idea of compression is to learn relationship between patterns of data which remains unchanged. The idea of decompression is to recover the original input from the compressed input. Since autoencoders are semi-supervised we have appended a fully connected network for computing degree for each labels. Our results show that DLDL shows the least mean-square error among the contemporary methods.
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Biswas, M., Suri, J.S. (2022). Deep-learning Based Autoencoder Model for Label Distribution Learning. In: Mukhopadhyay, S., Sarkar, S., Dutta, P., Mandal, J.K., Roy, S. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2022. Communications in Computer and Information Science, vol 1579. Springer, Cham. https://doi.org/10.1007/978-3-031-10766-5_5
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