Dual-Learning based Penalized Multi-Align Clustering for Multi-View Incomplete and Disorderly Data

Multimodal feature fusion, by integrating the complementary information from each modality, can effectively capture complex features in real-world data. However, in many use cases, such as boiler combustion monitoring, factors including equipment failure, inconsistent sensor sampling frequencies, and network delays often cause data collected from different modalities to suffer from missing modality and temporal asynchrony. This leads to the incompleteness and disorderliness of multimodal data. To address these issues, previous studies have proposed several data fusion methods that align the cluster centers before fusion. However, these approaches have two key limitations: 1) they do not guarantee a high alignment accuracy of data pairs at the sample level, and 2) they do not address the issue of significant discrepancies in data sizes across different classes, which impacts the subsequent data fusion performance.