Fast and Accurate Domain Adaptation for Irregular and Regular Tensor Decomposition

Abstract

Many real-world datasets including stock prices or disease records are represented as regular or irregular tensors across multiple domains. How can we accurately capture patterns from both irregular and regular tensors in a newly emerging domain by leveraging existing ones from multiple domains? This problem is crucial for applications such as identifying patterns of new diseases using data from existing ones. A main challenge is that the new target tensors contain limited information due to their recent emergence. Previously, PARAFAC2- and PARAFAC-based methods have been widely used to find patterns in irregular and regular tensors, respectively, through decomposing them into latent factors. However, they cannot effectively transfer knowledge from previously known tensors to the new one. In this work, we propose a fast and accurate domain adaptation method for tensor decomposition. We propose Meta-P2 for irregular tensors and Meta-P for regular tensors. Both Meta-P2 and Meta-P learn general and easily-adaptable information- - referred to as the meta factor-from multiple source domains. Using this meta factor, they efficiently identify patterns in a new target tensor. Extensive experiments on real-world datasets show that Meta-P2 and Meta-P achieve the state-of-the-art performance across various downstream tasks, including missing value prediction and anomaly detection.