Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data

• URL: http://arxiv.org/abs/2102.12679v1
• Date: Thu, 25 Feb 2021 04:39:13 GMT
• Title: Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
• Authors: Yu Gong and Hossein Hajimirsadeghi and Jiawei He and Thibaut Durand and Greg Mori
• Abstract summary: We propose the variational selective autoencoder (VSAE) to learn representations from partially-observed heterogeneous data. VSAE learns the latent dependencies in heterogeneous data by modeling the joint distribution of observed data, unobserved data, and the imputation mask. It results in a unified model for various downstream tasks including data generation and imputation.
• Score: 45.23338389559936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning from heterogeneous data poses challenges such as combining data from various sources and of different types. Meanwhile, heterogeneous data are often associated with missingness in real-world applications due to heterogeneity and noise of input sources. In this work, we propose the variational selective autoencoder (VSAE), a general framework to learn representations from partially-observed heterogeneous data. VSAE learns the latent dependencies in heterogeneous data by modeling the joint distribution of observed data, unobserved data, and the imputation mask which represents how the data are missing. It results in a unified model for various downstream tasks including data generation and imputation. Evaluation on both low-dimensional and high-dimensional heterogeneous datasets for these two tasks shows improvement over state-of-the-art models.

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