Abstract
Outlier detection helps users to identify the infrequent observations. Here, we propose a one-class classification model to detect outliers using autoencoder with deep SVDD. The support vector data description has been introduced to address the problem of outlier detection as it creates the smallest possible sphere around the given data points. The hybrid approach of using autoencoders with deep SVDD helps to achieve better results as autoencoders decrease data dimensions by training how to remove the noise present in the given data and compress and encode data which is given to deep SVDD for classification. Our result has been experimented on CIFAR10 complex dataset.
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Gupta, S., Boddu, S., Ambati, M. (2021). Outlier Detection Using One-Class Classification. In: Kumar, R., Dohare, R.K., Dubey, H., Singh, V.P. (eds) Applications of Advanced Computing in Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4862-2_24
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DOI: https://doi.org/10.1007/978-981-33-4862-2_24
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