Unsupervised Alignment

Unsupervised alignment focuses on aligning data from different sources or domains without relying on labeled data, aiming to discover underlying relationships and improve model performance across diverse datasets. Current research explores various algorithms, including those based on optimal transport, graph neural networks, and transformer architectures, to achieve this alignment across diverse data types such as embeddings, molecular structures, and time series data. This field is crucial for advancing machine learning applications in areas like biometrics, single-cell analysis, retrosynthesis prediction, and cross-lingual tasks, by enabling effective knowledge transfer and improved generalization across heterogeneous data sources. The development of scalable and robust unsupervised alignment methods is driving progress in many scientific and technological domains.