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Scalable Gaussian Process for Extreme classification
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en
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6
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Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020, pp. 1-6, Machine Learning for Signal Processing
Abstract
We address the limitations of Gaussian processes for multiclass classification in the setting where both the number of classes and the number of observations is very large. We propose a scalable approximate inference framework by combining the inducing points method with variational approximations of the likelihood that have been recently proposed in the literature. This leads to a tractable lower bound on the marginal likelihood that decomposes into a sum over both data points and class labels, and hence, is amenable to doubly stochastic optimization. To overcome memory issues when dealing with large datasets, we resort to amortized inference, which coupled with subsampling over classes reduces the computational and the memory footprint without a significant loss in performance. We demonstrate empirically that the proposed algorithm leads to superior performance in terms of test accuracy, and improved detection of tail labels.
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Dhaka, A, Andersen, M, Moreno, P & Vehtari, A 2020, Scalable Gaussian Process for Extreme classification. in Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020., 9231675, Machine Learning for Signal Processing, IEEE, pp. 1-6, IEEE International Workshop on Machine Learning for Signal Processing, Espoo, Finland, 21/09/2020. https://doi.org/10.1109/MLSP49062.2020.9231675