arXiv (All)
open access
Nonlinear Tensor Decomposition for Pattern Discovery
The CANDECOMP/PARAFAC (CP) decomposition is widely used for revealing the underlying patterns from multiway data (also referred to as a higher-order tensor). When the data is clipped or saturated, however, the linear CP model becomes unreliable. Several remedies exist such as treating the clipped en
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
- Large-Scale Bayesian Tensor Reconstruction via Approximate Message Passing#793440
- Large-Scale Bayesian Tensor Reconstruction via Approximate Message Passing#789436
- Tensor decomposition of transportation temporal and spatial big data: A brief review.#4392
- Identifiability of Nonnegative Tensor Decompositions via Positive Scattering#999123
- Low-Rank Tensor Decompositions for the Theory of Neural Networks: Understanding deep learning through the lens of tensor formats [Special Issue on the Mathematics of Deep Learning].#489682
Record · ID 1201352
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