Abstract
Dimensionality reduction refers to reducing the number of attributes that are being considered, by producing a set of principal variables. It can be divided into feature selection and feature extraction. Dimensionality reduction serves as one of the preliminary challenges in storage management and is useful for effective transmission over the Internet. In this paper, we propose a deep learning approach using encoder–decoder networks for effective (almost-lossless) compression and encryption. The neural network essentially encrypts data into an encoded format which can only be decrypted using the corresponding decoders. Clustering is essential to reduce the variation in the dataset to ensure overfit. Using clustering resulted in a net gain of 1% over the standard encoder architecture over three MNIST datasets. The compression ratio achieved was 24.6:1. The usage of image datasets is for visualization only and the proposed pipeline could be applied for textual and visual data as well.
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Acknowledgements
We would like to thank the providers of the publicly available datasets, which facilitated the comparisons reported in this paper. We would also like to thank the faculty of the Department of Information Technology, National Institute of Technology, Surathkal for insightful technical discussions.
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Mukesh, B.R., Madhumitha, N., Aditya, N.P., Vivek, S., Anand Kumar M. (2021). Clustering Enhanced Encoder–Decoder Approach to Dimensionality Reduction and Encryption. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_73
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DOI: https://doi.org/10.1007/978-981-15-5788-0_73
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