On Hierarchical Disentanglement of Interactive Behaviors for Multimodal Spatiotemporal Data with Incompleteness

Multimodal spatiotemporal data (MST) consists of multiple simultaneous spatiotemporal modalities that interact with each other in a dynamic manner. Due to the complexity of MST and the recent desire for the explainability of artificial intelligent systems, disentangled representation learning for MST (DisentMST) has become a significant task, which aims to learn disentangled representations that can expose the underlying spatial semantics, temporal dynamic patterns, and inter-modality interaction modes of the complex MST. One limitation of existing approaches is that they might fail to tolerate the real-world incomplete MST data, where missing information might break the cross-modal spatiotemporal dynamics and bring noise and ambiguity to the learning process. Another limitation is that no existing work systematically reveals the structure of different types of disentangled information. To tackle the two limitations, we define a novel two-level hierarchically structured disentanglement task for MST, which reveals informative and structured disentangled representations for MST as well as digests the real-world MST with incompleteness. We propose a new framework, BiDisentMST, which leverages Gaussian Processes and Graph Factorization on the latent space to achieve our purposes. The experimental results demonstrate the effectiveness of our proposed framework compared with baselines with respect to disentanglement and imputation results.