Subspace Kernel Learning on Tensor Sequences

1. 1Identify the tensor structure and modes in the data
2. 2Derive mode-wise subspaces from tensor unfoldings
3. 3Apply UKTL for similarity measurement and learning
4. 4Evaluate the performance of UKTL on large-scale tensor data

Data scientists and machine learning engineers working with multi-way data can benefit from UKTL for efficient and expressive similarity measures, while researchers can build upon this framework for further advancements

💡 UKTL enables expressive and robust similarity measures for multi-way data by comparing mode-wise subspaces

💡 UKTL: a novel kernel framework for tensor sequence learning