Abstract
Manifold learning is an efficient dimensionalilty reduction algorithm. But in real applications, difficulty lies in learning the parameters with limited supervised samples. Our proposed algorithm focuses on sparse representation of local linear preserving manifold dimensionality reduction algorithm and can solve the problem of unsupervised clustering. The manifold preserving methods take use of labeled data in manifold reduction except for the final classifier which produces unsupervised manifold reduction algorithm. Another solution for limited data is a novel proposed pretraining using Bayesian nets to construct the initial parameters for manifold learning, which is also robust to data w.r.t. uncertain perturbations. Then we show its validation in experiments and finally apply the algorithm for real world data. The algorithm performs better in noisy input with limited labeled data.
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Tenebaum, J.B., Silvam, V.D., Longford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)
Rowels, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)
Zhang, Z., Zha, H.: Principal manifolds and nonlinear dimension reduction via local tangent space alignment. SIAM J. Sci. Comput. 26, 313–338 (2004)
He, X., Niyongi, P.: Locality preserving projection. Proc. Neural Inf. Process. Syst. 14, 153 (2004)
Chen, S., Zhao, H., Kong, M., Luo, B.: 2D-LPP: a two-dimensional extension of locality pre-serving projections. Neurocomputing 70(4–6), 912–921 (2007)
Silva, J., Marques, J., Lemos, J.: Selecting landmark points for sparse manifold learning. In: Advances in neural information processing systems, pp. 1241–1248 (2005)
Law, M.H.C., Jain, A.K.: Incremental nonlinear dimensionality reduction by manifold learning. IEEE Trans. Pattern Anal. Mach. Intell. 28(3), 377–391 (2006)
Yan, S., Xu, D., Zhang, B., et al.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)
Zhang, J., Huang, H., Wang, J.: Manifold learning for visualizing and analyzing high-dimensional data. IEEE Intell. Syst. 25(4), 54–61 (2010)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Hinton, G.E., Roweis, S.T.: Stochastic neighbor embedding. In: Advances in Neural Information Processing Systems, vol. 15, pp. 833–840, Cambridge, MA, USA, The MIT Press (2002)
Usman, M., Vaillant, G., Atkinson, D., et al.: Compressive manifold learning: estimating one-dimensional respiratory motion directly from undersampled k-space data. Megnetic Reson. Med. 72(4), 1130–1140 (2014)
van der Maaten, L., Hinton, G.E.: Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
van der Maaten, L.: Learning a parametric embedding by preserving local structure. In: Proceedings of the Conference on Artificial Intelligence and Statistics (2009)
Yu, K., Zhang, T., Gong, Y.: Nonlinear learning using local coordinate coding. In: Proceedings of the Neural Information and Processing Systems (2009)
Yu, K., Zhang, T.: Improved local coordinate coding using local tangents. In: Proceedings of the International Conference on Machine Learning (2010)
Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Proceedings of the Neural Information and Processing Systems (2012)
Krizhevsky, A.: Learning multiple layers of features from tiny images. Technical report, University of Toronto (2009)
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Yang, Q., Sun, F. (2017). Unsupervised Local Linear Preserving Manifold Reduction with Uncertainty Pretraining for Image Recognition. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_47
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DOI: https://doi.org/10.1007/978-3-319-68345-4_47
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