Abstract
At present, most of the manifold learning methods use the K-Nearest Neighbor (KNN) criterion to determine the adjacency relationship between data points, which is not complete in the description of the sample distribution in the original data space. When many noise data are included in the observation space, the results of constructing adjacency graph by KNN may contain too many areas outside the unsupported domain, which will produce the wrong geometric projection distance. Isometric Feature Mapping (ISOMAP) and Locally Linear Embedding (LLE) algorithm are very sensitive to noise data because of above reason. In view of the above problem, a robust locally linear embedding method based on feature space projection is proposed. Based on LLE algorithm, the feature space projection is introduced to smooth the original samples and to improve the robustness of the noise data set in the process of reducing the dimension using LLE algorithm. The experimental results prove that this method is effectiveness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yan, H., Lu, J., Zhou, X.: Multi-feature multi-manifold learning for single-sample face recognition. Neurocomputing 143(16), 134–143 (2014)
Dornaika, F., Raduncanu, B.: Out-of-sample embedding for manifold learning applied to face recognition. In: Computer Vision and Pattern Recognition Workshops, pp. 862–868 (2014)
Berthet, Q., Rigollet, P.: Computational lower bounds for sparse PCA. In: Computer Science (2014)
Fernandes, S.L., Bala, G.J.: Recognizing facial images using ICA, LPP, MACE Gabor filters, score level fusion techniques. In: International Conference on Electronics and Communication Systems, pp. 1–5 (2014)
Zhang, D., Luo, T., Wang, D.: Learning from LDA using deep neural networks. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC-2016. LNCS (LNAI), vol. 10102, pp. 657–664. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50496-4_59
Guo, S., Li, P.Y., Wang, H.: LFA-based algorithm for IP network fast recovery. In: Computer Engineering & Design (2017)
Joseph, A.A., Tokumoto, T., Ozawa, S.: Online feature extraction based on accelerated kernel principal component analysis for data stream. Evol. Syst. 7(1), 15–27 (2016)
Li, J., Li, X., Tao, D.: KPCA for Semantic Object Extraction in Images. Elsevier Science Inc., New York (2008)
Yin, K.Z., Gong, W.G., Li, W.H.: Research on KICA-based face recognition. In: Computer Applications (2005)
Sudholt, S., Fink, G.A.: A modified Isomap approach to manifold learning in word spotting. In: Gall, J., Gehler, P., Leibe, B. (eds.) GCPR 2015. LNCS, vol. 9358, pp. 529–539. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24947-6_44
Yang, B., Xiang, M., Zhang, Y.: Multi-manifold discriminant Isomap for visualization and classification. Pattern Recognit. 55, 215–230 (2016)
Liu, F., Zhang, W., Gu, S.: Local linear Laplacian eigenmaps: a direct extension of LLE. Pattern Recognit. Lett. 75, 30–35 (2016)
Liu, Y., Yu, Z., Zeng, M.: LLE for submersible plunger pump fault diagnosis via joint wavelet and SVD approach. Neurocomputing 185, 202–211 (2016)
Wang, B., Yan, D., Chu, Y.: Face recognition based on sparse array of LPP and ELM. In: Microcomputer & Its Applications (2016)
Liu, Y.Q., Liang, J.G., Wang, Y.W.: Gain-improved double-slot LTSA with conformal corrugated edges. Int. J. RF Microw. Comput. Aided Eng. 1, e21133 (2017)
Cui, P., Zhang, X.: Generalized improvement of LTSA algorithm based on manifold learning. In: Computer Engineering & Applications (2017)
Xie, Y., Chenna, P., He, J.: Visualization of big high dimensional data in a three dimensional space. In: International Conference on Big Data Computing Applications and Technologies, pp. 61–66 (2017)
Lo, J.T., Gui, Y., Peng, Y.: Solving the local-minimum problem in training deep learning machines. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S. (eds.) ICONIP 2017. LNCS, vol. 10634, pp. 166–174. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70087-8_18
Bastani, O., Ioannou, Y., Lampropoulos, L.: Measuring neural net robustness with constraints (2016)
Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks, security and privacy (2017)
Chang, H., Yeung, D.Y.: Robust locally linear embedding. Pattern Recognit. 39(6), 1053–1065 (2006)
Alexa, M., Adamson, A.: On normals and projection operators for surfaces defined by point sets. In: Eurographics Conference on Point-Based Graphics, pp. 149–155 (2004)
Zhang, S., Li, X., Zong, M.: Efficient kNN classification with different numbers of nearest neighbors. IEEE Trans. Neural Netw. Learn. Syst. 99, 1–12 (2017)
Adeniyi, D.A., Wei, Z., Yong, Y.: Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Appl. Comput. Inform. 12(1), 90–108 (2016)
Acknowledgments
This work was partly supported by the grants of Natural Science Foundation of China (61273303&61572381).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Zou, FM., Li, B., Fan, ZT. (2018). A Robust Locally Linear Embedding Method Based on Feature Space Projection. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_76
Download citation
DOI: https://doi.org/10.1007/978-3-319-95957-3_76
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95956-6
Online ISBN: 978-3-319-95957-3
eBook Packages: Computer ScienceComputer Science (R0)