Abstract
This paper proposes an invertible nonlinear dimensionality reduction method via jointly learning dictionaries in both the original high dimensional data space and its low dimensional representation space. We construct an appropriate cost function, which preserves inner products of data representations in the low dimensional space. We employ a conjugate gradient algorithm on smooth manifold to minimize the cost function. By numerical experiments in image processing, our proposed method provides competitive and robust performance in image compression and recovery, even on heavily corrupted data. In other words, it can also be considered as an alternative approach to compressed sensing. While our approach can outperform compressed sensing in task-driven learning problems, such as data visualization.
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Van der Maaten, L.J., Postma, E.O., van den Herik, H.J.: Dimensionality reduction: a comparative review. J. Mach. Learn. Res. 10(1–41), 66–71 (2009)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
Hawe, S., Seibert, M., Kleinsteuber, M.: Separable dictionary learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 438–445. IEEE (2013)
Gleichman, S., Eldar, Y.C.: Blind compressed sensing. IEEE Trans. Inf. Theory 57(10), 6958–6975 (2011)
Carvajalino, J.M.D., Sapiro, G.: Learning to sense sparse signals: simultaneous sensing matrix and sparsifying dictionary optimization. IEEE Trans. Image Process. 18(7), 1395–1408 (2009)
Elad, M.: Optimized projections for compressed sensing. IEEE Trans. Signal Process. 55(12), 5695–5702 (2007)
Calderbank, R., Jafarpour, S., Schapire, R.: Compressed learning: Universal sparse dimensionality reduction and learning in the measurement domain. Technical report, Computer Science, Princeton University (2009)
Zeyde, R., Elad, M., Protter, M.: On Single Image Scale-Up Using Sparse-Representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces 2011. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012)
Johnson, W.B., Lindenstrauss, J.: Extensions of Lipschitz mappings into a Hilbert space. Contemp. Math. 26(189–206), 1 (1984)
Kim, H., Park, H., Zha, H.: Distance preserving dimension reduction for manifold learning. In: SDM, SIAM, pp. 527–532 (2007)
Baraniuk, R., Davenport, M., DeVore, R., Wakin, M.: A simple proof of the restricted isometry property for random matrices. Constructive Approximation 28(3), 253–263 (2008)
Elad, M.: Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer, New York (2010)
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. Ser. B (Stat. Methodol.) 67(2), 301–320 (2005)
Mairal, J., Bach, F., Ponce, J.: Task-driven dictionary learning. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 791–804 (2012)
Wei, X., Shen, H., Kleinsteuber, M.: An adaptive dictionary learning approach for modeling dynamical textures. In: Proceedings of the \(39^{th}\) IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 3567–3571 (2014)
Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. In: Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp. 46–51. IEEE (2002)
De la Torre, F., Black, M.J.: Robust principal component analysis for computer vision. In: Eighth IEEE International Conference on Computer Vision (ICCV), vol. 1, pp. 362–369. IEEE (2001)
Ji, S., Xue, Y., Carin, L.: Bayesian compressive sensing. IEEE Trans. Signal Process. 56(6), 2346–2356 (2008)
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Wei, X., Kleinsteuber, M., Shen, H. (2015). Invertible Nonlinear Dimensionality Reduction via Joint Dictionary Learning. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_32
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DOI: https://doi.org/10.1007/978-3-319-22482-4_32
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