Supervised locally linear embedding algorithm based on orthogonal matching pursuit

Supervised locally linear embedding (SLLE) has been proposed for classification tasks. SLLE can take full use of the label information and select neighbours only in the same class. However, SLLE uses the least squares (LSs) method for solving a set of linear equations to obtain linear representation...

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Bibliographic Details
Published in:IET image processing Vol. 9; no. 8; pp. 626 - 633
Main Authors: Zhang, Li, Leng, Yiqin, Yang, Jiwen, Li, Fanzhang
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 01.08.2015
Subjects:
Classification
classification tasks
image classification
image reconstruction
Inverse
learning (artificial intelligence)
Least squares method
Linear equations
linear representation coefficients
Matching
matrix algebra
orthogonal matching pursuit
Remedies
Representations
SLLE based on orthogonal matching pursuit
SLLE‐OMP
supervised locally linear embedding algorithm
Tasks
image classification
classification tasks
SLLE-OMP
image reconstruction
learning (artificial intelligence)
supervised locally linear embedding algorithm
orthogonal matching pursuit
linear representation coefficients
SLLE based on orthogonal matching pursuit
matrix algebra
linear equations
ISSN:1751-9659, 1751-9667
Online Access:Get full text
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Summary:Supervised locally linear embedding (SLLE) has been proposed for classification tasks. SLLE can take full use of the label information and select neighbours only in the same class. However, SLLE uses the least squares (LSs) method for solving a set of linear equations to obtain linear representation coefficients, which relates to the inverse of a matrix. If the matrix is singular, the solution to the set of linear equations does not exist. Additionally, if the size of neighbourhood is not appropriate, some further neighbours along the manifold would be selected. To remedy those, this study deals with SLLE based on orthogonal matching pursuit (SLLE-OMP) by introducing OMP into SLLE. In SLLE-OMP, LS is replaced by OMP and OMP can reselect new neighbours from old ones. Experimental results on some real-world datasets show that SLLE-OMP can achieve better classification performance compared with SLLE.
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ISSN:1751-9659
1751-9667
DOI:10.1049/iet-ipr.2014.0841