Nazabal Renteria, AlfredoMartínez Olmos, PabloGhahramani, ZoubinValera Martínez, María Isabel2021-05-252022-11-012020-11Nazábal, A., Olmos, P. M., Ghahramani, Z. & Valera, I. (2020). Handling incomplete heterogeneous data using VAEs. Pattern Recognition, vol. 107, 107501.0031-3203https://hdl.handle.net/10016/32743Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data. However, existing VAEs can still not directly handle data that are heterogenous (mixed continuous and discrete) or incomplete (with missing data at random), which is indeed common in real-world applications. In this paper, we propose a general framework to design VAEs suitable for fitting incomplete heterogenous data. The proposed HI-VAE includes likelihood models for real-valued, positive real valued, interval, categorical, ordinal and count data, and allows accurate estimation (and potentially imputation) of missing data. Furthermore, HI-VAE presents competitive predictive performance in supervised tasks, outperforming supervised models when trained on incomplete data.11eng© 2020 Elsevier Ltd.Atribución-NoComercial-SinDerivadas 3.0 EspañaGenerative modelsVariational autoencodersIncomplete heterogenous dataresearch articleTelecomunicacioneshttps://doi.org/10.1016/j.patcog.2020.107501open access107501Pattern Recognition107AR/0000027141