Manifold Learning in Local Tangent Space Via Extreme Learning Machine
Abstract
In This Paper, We Propose a Fast Manifold Learning Strategy to Estimate the Underlying Geometrical Distribution and Develop the Relevant Mathematical Criterion on the Basis of the Extreme Learning Machine (Elm) in the High-Dimensional Space. the Local Tangent Space Alignment (Ltsa) Method Has Been Used to Perform the Manifold Production and the Single Hidden Layer Feedforward Network (Slfn) is Established Via Elm to Simulate the Low-Dimensional Representation Process. the Scheme of the Elm Ensemble Then Combines the Individual Slfn for the Model Selection, Where the Manifold Regularization Mechanism Has Been Brought into Elm to Preserve the Local Geometrical Structure of Ltsa. Some Developments Have Been Done to Evaluate the Inherent Representation Embedding in the Elm Learning. the Simulation Results Have Shown the Excellent Performance in the Accuracy and Efficiency of the Developed Approach.
Recommended Citation
Q. Wang et al., "Manifold Learning in Local Tangent Space Via Extreme Learning Machine," Neurocomputing, vol. 174, pp. 18 - 30, Elsevier, Jan 2016.
The definitive version is available at https://doi.org/10.1016/j.neucom.2015.03.116
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Extreme learning machine; High-dimensional space; Local tangent space alignment; Manifold learning
International Standard Serial Number (ISSN)
1872-8286; 0925-2312
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
22 Jan 2016
Comments
National Natural Science Foundation of China, Grant 2013GHY11507