Skip to search formSkip to main contentSkip to account menu

Robust principal component analysis

Known as: Robust PCA 
Robust Principal Component Analysis (RPCA) is a modification of the widely used statistical procedure of principal component analysis (PCA) which… 
Wikipedia (opens in a new tab)

Related topics

Related topics

3 relations
Background subtractionLambertian reflectancePrincipal component analysis

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2017
Highly Cited
2017
Accelerated Alternating Projections for Robust Principal Component Analysis
We study robust PCA for the fully observed setting, which is about separating a low rank matrix $\boldsymbol{L}$ and a sparse… 
Highly Cited
2016
Highly Cited
2016
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization
This paper studies the Tensor Robust Principal Component (TRPCA) problem which extends the known Robust PCA [4] to the tensor… 
Highly Cited
2015
Highly Cited
2015
Robust Principal Component Analysis on Graphs
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is… 
Highly Cited
2014
Highly Cited
2014
Optimal Mean Robust Principal Component Analysis
Principal Component Analysis (PCA) is the most widely used unsupervised dimensionality reduction approach. In recent research… 
Highly Cited
2014
Highly Cited
2014
Robust Principal Component Analysis with Complex Noise
The research on robust principal component analysis (RPCA) has been attracting much attention recently. The original RPCA model… 
Highly Cited
2013
Highly Cited
2013
Robust principal component analysis via capped norms
In many applications such as image and video processing, the data matrix often possesses simultaneously a low-rank structure… 
Highly Cited
2012
Highly Cited
2012
Singing-voice separation from monaural recordings using robust principal component analysis
Separating singing voices from music accompaniment is an important task in many applications, such as music information retrieval… 
Highly Cited
2009
Highly Cited
2009
Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization
Principal component analysis is a fundamental operation in computational data analysis, with myriad applications ranging from web… 
Highly Cited
2005
Highly Cited
2005
ROBPCA: A New Approach to Robust Principal Component Analysis
We introduce a new method for robust principal component analysis (PCA). Classical PCA is based on the empirical covariance… 
Review
2001
Review
2001
Robust principal component analysis for computer vision
Principal Component Analysis (PCA) has been widely used for the representation of shape, appearance and motion. One drawback of… 
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)