Subspace Estimation Under Coarse Quantization
MCML Authors
Johannes Maly
Prof. Dr.
Collaborating PI
Abstract
Johannes Maly
Prof. Dr.
Collaborating PI
Abstract
We study subspace estimation from coarsly quantized data. In particular, we analyze two stochastic quantization schemes which use dithering: a one-bit quantizer combined with rectangular dither and a multi-bit quantizer with triangular dither. For each quantizer, we derive rigorous high probability bounds for the distances between the true and estimated signal subspaces. Using our analysis, we identify scenarios in which subspace estimation via triangular dithering qualitatively outperforms rectangular dithering. We verify in numerical simulations that our estimates are optimal in their dependence on the smallest non-zero eigenvalue of the target matrix.
inproceedings DLM25
SampTA 2025
15th International Conference on Sampling Theory and Applications. Vienna, Austria, Jul 28-Aug 01, 2025.Authors
S. Dirksen • W. Li • J. MalyLinks
DOIResearch Area
BibTeXKey: DLM25