arXiv (All)
open access
Multilinear Common Component Analysis via Kronecker Product Representation
We consider the problem of extracting a common structure from multiple tensor datasets. For this purpose, we propose multilinear common component analysis (MCCA) based on Kronecker products of mode-wise covariance matrices. MCCA constructs a common basis represented by linear combinations of the ori
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 (machine learning)#343076
- Tensor network-based gene regulatory network inference for single-cell transcriptomic data.#362163
- Fast and Accurate CP-HIFI Tensor Decompositions: Exploiting Kronecker Structure#629952
- Fast and Accurate CP-HIFI Tensor Decompositions: Exploiting Kronecker Structure#628090
- Lindblad Multiproduct Formulas#971979
Record · ID 1036499
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