Large scale multi-label learning using Gaussian processes

作者:Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias

摘要

We introduce a Gaussian process latent factor model for multi-label classification that can capture correlations among class labels by using a small set of latent Gaussian process functions. To address computational challenges, when the number of training instances is very large, we introduce several techniques based on variational sparse Gaussian process approximations and stochastic optimization. Specifically, we apply doubly stochastic variational inference that sub-samples data instances and classes which allows us to cope with Big Data. Furthermore, we show it is possible and beneficial to optimize over inducing points, using gradient-based methods, even in very high dimensional input spaces involving up to hundreds of thousands of dimensions. We demonstrate the usefulness of our approach on several real-world large-scale multi-label learning problems.

论文关键词:Multi-label learning, Gaussian process, Variational inference, Bayesian nonparametrics

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论文官网地址:https://doi.org/10.1007/s10994-021-05952-5