Alignment and stability of embeddings: measurement and inference improvement
This work addresses a previously undefined issue in representation learning that affects the reliability of embeddings for dynamic systems, though it is incremental as it formalizes and measures an existing problem.
The paper tackles the problem of misaligned embeddings in representation learning, which can degrade performance in dynamic network inference tasks, and shows that ensuring alignment improves prediction accuracy by up to 90% in static and 40% in dynamic methods.
Representation learning (RL) methods learn objects' latent embeddings where information is preserved by distances. Since distances are invariant to certain linear transformations, one may obtain different embeddings while preserving the same information. In dynamic systems, a temporal difference in embeddings may be explained by the stability of the system or by the misalignment of embeddings due to arbitrary transformations. In the literature, embedding alignment has not been defined formally, explored theoretically, or analyzed empirically. Here, we explore the embedding alignment and its parts, provide the first formal definitions, propose novel metrics to measure alignment and stability, and show their suitability through synthetic experiments. Real-world experiments show that both static and dynamic RL methods are prone to produce misaligned embeddings and such misalignment worsens the performance of dynamic network inference tasks. By ensuring alignment, the prediction accuracy raises by up to 90% in static and by 40% in dynamic RL methods.