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Activity Number: 70 - Multivariate Statistical Methods
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313805
Title: Interpretable Recurrent Nonlinear Group Factor Analysis
Author(s): Lin Qiu* and Vernon M. Chinchilli and Lin Lin
Companies: and The Pennsylvania State University and The Pennsylvania State University
Keywords: Group factor analysis ; latent variable model ; deep neural network; longitudinal ; generative model

In many scientific problems such as video surveillance, modern genomic analysis, and clinical studies, data are often collected from diverse domains across time that exhibit time-dependent heterogeneous properties. It is important to not only integrate data from multiple sources (called multi-view data), but also to incorporate time dependency for deep understanding of the underlying system. Latent factor models are popular exploratory tools of group complex data. However, it is frequently observed that these models do not perform well for complex systems and they are not applicable to time-series data. Therefore, we propose a generative model based on variational autoencoder and recurrent neural network to infer the latent dynamic factors for multivariate time-series data. This approach allows us to identify the disentangled latent embeddings across multiple modalities while accounting for the time factor. We invoke our proposed model for analyzing three datasets on which we demonstrate the effectiveness and the interpretability of the model.

Authors who are presenting talks have a * after their name.

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