Skip to main navigation
Skip to search
Skip to main content
Princeton University Home
Help & FAQ
Home
Profiles
Research units
Facilities
Projects
Research output
Press/Media
Search by expertise, name or affiliation
Steerable ePCA: Rotationally Invariant Exponential Family PCA
Zhizhen Zhao
,
Lydia T. Liu
,
Amit Singer
Mathematics
Center for Statistics & Machine Learning
Research output
:
Contribution to journal
›
Article
›
peer-review
2
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Steerable ePCA: Rotationally Invariant Exponential Family PCA'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Mathematics
Principal Component Analysis
100%
Exponential Family
100%
Covariance
57%
Free Electron
28%
Numerical Experiment
14%
Principal Components
14%
Poisson Distribution
14%
Covariance Matrix
14%
Covariance Matrix Estimation
14%
Exponential Distribution Family
14%
Agricultural and Biological Sciences
Principal Component Analysis
100%
Covariance
85%
Face
28%
Poisson Distribution
14%
Physics
Covariance
100%
X-Ray Free Electron Laser
33%
Molecular Structure
16%
Keyphrases
Planar Rotation
28%
Engineering
Face Image
12%