In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages
Recently, a new technique called 2-dimensional principal component analysis (2DPCA) was proposed for...
The principal component analysis (PCA), or the eigenfaces method, is a de facto standard in human fa...
The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Muc...
Abstract—Traditional bidirectional two-dimension (2D) principal component analysis ((2D)2PCA-L2) is ...
A method of principal component analysis (PCA) based on a new L1-norm optimization technique is prop...
Two-dimensional principle component analysis (2DPCA) has been widely used for face image representat...
Summarization: Standard Principal-Component Analysis (PCA) is known to be very sensitive to outliers...
We extensively investigate robust sparse two dimensional principal component analysis (RS2DPCA) that...
<p> Block principal component analysis with l(1)-norm (BPCA-L1) has demonstrated its effectiveness ...
Principal component analysis (PCA) minimizes the sum of squared errors (L2-norm) and is sensitive to...
© 2018 IEEE. Principal component analysis (PCA) is widely used methods for dimensionality reduction ...
Most existing robust principal component analysis (PCA) involve mean estimation for extracting low-d...
to appearInternational audiencePrincipal component analysis (PCA) based on L1- norm maximization is ...
The best-fit subspace or low-rank approximation of a data matrix revolvesaround the norm approximati...
This paper proposes several principal component analysis methods based on Lp-norm optimiza-tion tech...
Recently, a new technique called 2-dimensional principal component analysis (2DPCA) was proposed for...
The principal component analysis (PCA), or the eigenfaces method, is a de facto standard in human fa...
The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Muc...
Abstract—Traditional bidirectional two-dimension (2D) principal component analysis ((2D)2PCA-L2) is ...
A method of principal component analysis (PCA) based on a new L1-norm optimization technique is prop...
Two-dimensional principle component analysis (2DPCA) has been widely used for face image representat...
Summarization: Standard Principal-Component Analysis (PCA) is known to be very sensitive to outliers...
We extensively investigate robust sparse two dimensional principal component analysis (RS2DPCA) that...
<p> Block principal component analysis with l(1)-norm (BPCA-L1) has demonstrated its effectiveness ...
Principal component analysis (PCA) minimizes the sum of squared errors (L2-norm) and is sensitive to...
© 2018 IEEE. Principal component analysis (PCA) is widely used methods for dimensionality reduction ...
Most existing robust principal component analysis (PCA) involve mean estimation for extracting low-d...
to appearInternational audiencePrincipal component analysis (PCA) based on L1- norm maximization is ...
The best-fit subspace or low-rank approximation of a data matrix revolvesaround the norm approximati...
This paper proposes several principal component analysis methods based on Lp-norm optimiza-tion tech...
Recently, a new technique called 2-dimensional principal component analysis (2DPCA) was proposed for...
The principal component analysis (PCA), or the eigenfaces method, is a de facto standard in human fa...
The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Muc...
DOI
Add to ORKG