Incomplete Multi View Data

Incomplete multi-view data, characterized by missing information across different data representations of the same objects, presents a significant challenge in machine learning. Current research focuses on developing robust methods for handling this incompleteness, often employing techniques like tensor decomposition, graph-based approaches (including heterogeneous graph networks), and matrix factorization to integrate and impute missing data while simultaneously performing tasks such as feature selection or clustering. These advancements aim to improve the accuracy and reliability of analyses performed on incomplete datasets, impacting various applications where data are naturally incomplete or subject to missingness.