Also known as Hierarchical Tucker (HT) decomposition.
A Machine Learning technique that structures high-dimensional, complex, or incomplete data, into a hierarchical, low-rank format.
- Employed to complete missing data in high-dimensional tensors by exploiting their low-rank structures.
- Allows for significant data compression.
How it works:
Breaks a large d-dimensional tensor into a Binary Tree of smaller, manageable operations.