arXiv (All)
open access
Geometric Fluctuations of the $\sinΘ$ Distance in High-Dimensional Principal Subspace Estimation
We investigate the geometric fluctuations of principal subspaces for high-dimensional covariance matrices through the squared Frobenius $\sinΘ$ distance between the sample and population eigenspaces associated with the $r_p$ largest eigenvalues. An explicit first-order expansion and a central limit
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
- The anisotropic local law for sample covariance matrices under quadratic-form concentration#999897
- Diagonal Attenuation: A Finite-Sample Correction for PCA#973426
- Fast algorithms for learning a Gaussian under halfspace truncation with optimal sample complexity#310805
- A random matrix model with localization and ergodic transitions#1097545
- An Exact Tail Condition for the Largest Error in Estimating a Rank-One Direction#972182
Record · ID 984651
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