Improved Algorithm Base on Locally Linear Embedding

Shi Min Liu; Yan Ni Deng; Yuan Xing Lv

doi:10.4028/www.scientific.net/AMM.644-650.2160

Paper Titles

Scheduling Algorithm for High-Mobility User in LTE Downlink System
p.2143

Adaptive Clone Selection Algorithm for Function Optimization
p.2147

The Application of Minimum Spanning Tree Clustering in the Ontology Mapping Pretreatment
p.2151

Research on the High-Speed Railway Energy-Saving Operation Strategy Based on Heuristic Algorithm
p.2155

Improved Algorithm Base on Locally Linear Embedding
p.2160

Study on Data Classification and Trend of Association Rules of Time Series
p.2164

An Improved Novel Global Harmony Search Algorithm
p.2169

Normal Parameter Reduction in Soft Set Based on Harmony Search Algorithm
p.2173

Hemming Process Simulation of Table-Top Hemming Machine
p.2177

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650Improved Algorithm Base on Locally Linear...

Improved Algorithm Base on Locally Linear Embedding

Abstract:

Locally linear embedding algorithm (LLE) , It makes up the shortcomings that the manifold learning algorithm can be only applied to training samples but not be extended to test samples . However, due to the presence of its Low-dimensional feature space redundant information,and its sample category information does not integrate into a low-dimensional embedding. For this shortcoming, here we introduce the two improved algorithms:the local linear maximum dispersion matrix algorithm (FSLLE) and the adaptive algorithm (ALLE), and the combinations of the above two algorithms.With this experience,combined Garbol and locally linear embedding algorithm (LLE) to compare each conclusion. The results proved to be effective elimination of redundant information among basis vectors and improve the recognition rate.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

2160-2163

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.2160

Citation:

Cite this paper

Online since:

September 2014

Authors:

Shi Min Liu*, Yan Ni Deng, Yuan Xing Lv

Keywords:

ALLE, Face Recognition, FSLLE, LLE

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] G. H GoLub and C. F Van Loan. Matrix Computation Press, Baltimore, Maryland, 3nd edition, (1996).

Google Scholar

[2] Wang H Y, Zheng J. Application of clustering and improved LLE based on data from the dimensionality reduction. Computer Research and Development 2006, val. 43(8): 1485-1490P.

Google Scholar

[3] Mallat. A Wavelet Tour of Signal Processing. Second Edition. Academic Press, (1999).

Google Scholar

[4] Gabor D. Theory of communication. Journal of the Institute of ElectricalEngineers, 1946, 93(26): 429-457P.

Google Scholar

[5] Daugman J. G. Two-Dimensional Spectral Analysis of Cortical Receptive Field Profile. Vision Research, 1980, 20: 847-856P.

DOI: 10.1016/0042-6989(80)90065-6

Google Scholar

[6] Zhong M, Xue H F, Mei M. Maximum scatter matrix algorithms locally linear embedding. Computer Engineering, 2011, val. 37(12): 176-181P.

Google Scholar

[7] Lee T. S. Image representation using 2D Gabor wavelets. IEEE Trans Pattern Analysis and Machine Intelligence, 1996, 18(10): 959-971P.

DOI: 10.1109/34.541406

Google Scholar