arXiv (All)
open access
Tensor Orthogonal Subspace Split: Theory and Applications
Tensor representations have emerged as a fundamental paradigm for modeling multidimensional data by preserving intrinsic correlations across multiple modes. This paper proposes a novel theoretical framework, termed Tensor Orthogonal Subspace Split (TOSS), which explicitly splits a tensor, along a pr
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Tensor Orthogonal Subspace Split: Theory and Applications#796864
- Tensor (machine learning)#343076
- Bayesian Tensor Regression for Neuroimaging Data#1064424
- Duality of Orthogonal and Symplectic Random Tensor Models#677295
- Duality of Orthogonal and Symplectic Random Tensor Models#674098
Record · ID 798277
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive