Multimodal hierarchical Variational AutoEncoders with Factor Analysis latent space

• URL: http://arxiv.org/abs/2207.09185v3
• Date: Tue, 22 Oct 2024 08:17:21 GMT
• Title: Multimodal hierarchical Variational AutoEncoders with Factor Analysis latent space
• Authors: Alejandro Guerrero-López, Carlos Sevilla-Salcedo, Vanessa Gómez-Verdejo, Pablo M. Olmos,
• Abstract summary: This study proposes a novel method to address limitations by combining Variational AutoEncoders (VAEs) with a Factor Analysis latent space (FA-VAE) The proposed FA-VAE method employs multiple VAEs to learn a private representation for each heterogeneous data view in a continuous latent space.
• Score: 45.418113011182186
• License:
• Abstract: Purpose: Handling heterogeneous and mixed data types has become increasingly critical with the exponential growth in real-world databases. While deep generative models attempt to merge diverse data views into a common latent space, they often sacrifice interpretability, flexibility, and modularity. This study proposes a novel method to address these limitations by combining Variational AutoEncoders (VAEs) with a Factor Analysis latent space (FA-VAE). Methods: The proposed FA-VAE method employs multiple VAEs to learn a private representation for each heterogeneous data view in a continuous latent space. Information is shared between views using a low-dimensional latent space, generated via a linear projection matrix. This modular design creates a hierarchical dependency between private and shared latent spaces, allowing for the flexible addition of new views and conditioning of pre-trained models. Results: The FA-VAE approach facilitates cross-generation of data from different domains and enables transfer learning between generative models. This allows for effective integration of information across diverse data views while preserving their distinct characteristics. Conclusions: By overcoming the limitations of existing methods, the FA-VAE provides a more interpretable, flexible, and modular solution for managing heterogeneous data types. It offers a pathway to more efficient and scalable data-handling strategies, enhancing the potential for cross-domain data synthesis and model transferability.

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