arXiv (All)
open access
Functional Principal Component Analysis for Manifold-Indexed Data
Functional principal component analysis has traditionally focused on processes indexed by Euclidean domains, although functional observations increasingly arise on curved domains. We develop FPCA for real-valued processes observed with noise at discrete locations on a general Riemannian manifold. We
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
- On Some Theoretical Aspects of Functional Data Analysis of Parametrized Curves in $\mathbb{R}^p$#1076345
- Topological Data Analysis for High-Dimensional Dynamic Process Monitoring#290578
- Natural Riemannian gradient for learning functional tensor networks#5980
- Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data#138963
- Spatio-Temporal Autoregressions for High Dimensional Matrix-Valued Time Series#617853
Record · ID 1343629
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