Online TT-ALS for Streaming Tensor Decomposition with Incremental Orthogonalization
Tensor Train (TT) decomposition is a powerful technique for analyzing high-dimensional data. Existing algorithms for computing TT decompositions can be categorized into two main types: conventional batch-based approaches and recursive online methods. In the context of streaming data, batch methods typically achieve higher reconstruction accuracy but often suffer from memory exhaustion, while online methods provide greater computational efficiency. In this work, we introduce Online TT-ALS (Alternating Least Squares), an algorithm that sequentially enforces orthogonality constraints. This approach allows for efficient and exact updates of the core tensor while maintaining high reconstructio...
| Authors | Hiroki Takeda, Yuto Miyatake, Daisuke Furihata |
|---|---|
| Primary category | math.NA |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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