Paper Info
Title
Out-of-Sample embedding of spherical manifold based on constrained least squares
Abstract
All of the current state-of-the-art nonlinear dimensionality reduction methods attempt to seek the low-dimensional manifold structure by preserving global or local properties of the original data, but without considering the constraint of the manifold structure, thus, there may be a big contrast between the manifold structure result obtained by the nonlinear techniques and the result that we expected. Therefore, it is necessary for us to study the constrained nonlinear dimensionality reduction. In this paper, we study the embedding of a hidden manifold onto a unit sphere by using SMACOF algorithm and propose a method to solve the out-of-sample problem which usually arises in the manifold learning. By converting it into a constrained least squares problem with the spherical structure information, this method avoids reconstructing the neighborhood graph. The application results of 3-D object pose estimation show the effectiveness of our propose method.
Year
DOI
Venue
2011
10.1007/978-3-642-31919-8_72
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Keywords
Field
DocType
current state-of-the-art nonlinear dimensionality,manifold structure result,manifold learning,nonlinear technique,nonlinear dimensionality reduction,spherical structure information,low-dimensional manifold structure,out-of-sample embedding,reduction method,manifold structure,spherical manifold,hidden manifold
Local tangent space alignment,Topology,Embedding,Nonlinear system,Pose,Manifold alignment,Nonlinear dimensionality reduction,Manifold,Mathematics,Unit sphere
Conference
Volume
Issue
ISSN
7202 LNCS
null
16113349
Citations 
PageRank 
References 
0
0.34
6
Authors
5
Name
Order
Citations
PageRank
Yongpeng Zhang113513.37
Zenggang Lin2143.61
Rui Yao3261.22
Yu Zhu48812.65
Haisen Li543.24