Subspace Kernel Learning on Tensor Sequences
📰 ArXiv cs.AI
UKTL is a novel kernel framework for learning from tensor sequences by comparing mode-wise subspaces
Action Steps
- Identify the tensor structure and modes in the data
- Derive mode-wise subspaces from tensor unfoldings
- Apply UKTL for similarity measurement and learning
- Evaluate the performance of UKTL on large-scale tensor data
Who Needs to Know This
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
Key Insight
💡 UKTL enables expressive and robust similarity measures for multi-way data by comparing mode-wise subspaces
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💡 UKTL: a novel kernel framework for tensor sequence learning
Key Takeaways
UKTL is a novel kernel framework for learning from tensor sequences by comparing mode-wise subspaces
Full Article
Title: Subspace Kernel Learning on Tensor Sequences
Abstract:
arXiv:2603.19546v1 Announce Type: cross Abstract: Learning from structured multi-way data, represented as higher-order tensors, requires capturing complex interactions across tensor modes while remaining computationally efficient. We introduce Uncertainty-driven Kernel Tensor Learning (UKTL), a novel kernel framework for $M$-mode tensors that compares mode-wise subspaces derived from tensor unfoldings, enabling expressive and robust similarity measure. To handle large-scale tensor data, we propo
Abstract:
arXiv:2603.19546v1 Announce Type: cross Abstract: Learning from structured multi-way data, represented as higher-order tensors, requires capturing complex interactions across tensor modes while remaining computationally efficient. We introduce Uncertainty-driven Kernel Tensor Learning (UKTL), a novel kernel framework for $M$-mode tensors that compares mode-wise subspaces derived from tensor unfoldings, enabling expressive and robust similarity measure. To handle large-scale tensor data, we propo
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