Research PaperResearchia:202609.28004
Gap-free Differentially Private PCA for Gaussian Data
Alina Ene
Abstract
We give a gap-free differentially private algorithm for the principal component analysis (PCA) problem with Gaussian data. --- Source: arXiv:2609.31614v1 - http://arxiv.org/abs/2609.31614v1 PDF: https://arxiv.org/pdf/2609.31614v1 Original Link: http://arxiv.org/abs/2609.31614v1
Submitted: September 28, 2026Subjects: Machine Learning; Data Science
Description / Details
We give a gap-free differentially private algorithm for the principal component analysis (PCA) problem with Gaussian data.
Source: arXiv:2609.31614v1 - http://arxiv.org/abs/2609.31614v1 PDF: https://arxiv.org/pdf/2609.31614v1 Original Link: http://arxiv.org/abs/2609.31614v1
Please sign in to join the discussion.
No comments yet. Be the first to share your thoughts!
Access Paper
Submission Info
Date:
Sep 28, 2026
Sep 28, 2026
Topic:
Data Science
Data Science
Area:
Machine Learning
Machine Learning
Comments:
0
0
Bookmark