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General risk measures for robust machine learning
Learning by active nonlinear diffusion
1. | Department of Mathematics, Department of Applied Mathematics and Statistics, Mathematical Institute of Data Sciences, Institute of Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, MD 21218, USA |
2. | Department of Mathematics, Tufts University, Medford, MA 02155, USA |
This article proposes an active learning method for high-dimensional data, based on intrinsic data geometries learned through diffusion processes on graphs. Diffusion distances are used to parametrize low-dimensional structures on the dataset, which allow for high-accuracy labelings with only a small number of carefully chosen training labels. The geometric structure of the data suggests regions that have homogeneous labels, as well as regions with high label complexity that should be queried for labels. The proposed method enjoys theoretical performance guarantees on a general geometric data model, in which clusters corresponding to semantically meaningful classes are permitted to have nonlinear geometries, high ambient dimensionality, and suffer from significant noise and outlier corruption. The proposed algorithm is implemented in a manner that is quasilinear in the number of unlabeled data points, and exhibits competitive empirical performance on synthetic datasets and real hyperspectral remote sensing images.
References:
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M.-F. Balcan, A. Broder and T. Zhang, Margin based active learning, in International Conference on Computational Learning Theory, Springer, 4359 (2007), 35–50.
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Diffusion maps, Applied and Computational Harmonic Analysis, 21 (2006), 5-30.
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R. Coifman, S. Lafon, A. Lee, M. Maggioni, B. Nadler, F. Warner and S. Zucker, Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps, Proceedings of the National Academy of Sciences of the United States of America, 102 (2005), 7426-7431. Google Scholar |
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Hierarchical clustering of hyperspectral images using rank-two nonnegative matrix factorization, IEEE Transactions on Geoscience and Remote Sensing, 53 (2015), 2066-2078.
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show all references
References:
[1] |
N. Acito, G. Corsini and M. Diani, An unsupervised algorithm for hyperspectral image segmentation based on the gaussian mixture model, in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 6 (2003), 3745–3747.
doi: 10.1109/IGARSS.2003.1295256. |
[2] |
A. Anis, A. Gadde and A. Ortega, Towards a sampling theorem for signals on arbitrary graphs, in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2014, 3864–3868.
doi: 10.1109/ICASSP.2014.6854325. |
[3] |
A. Anis, A. Gadde and A. Ortega,
Efficient sampling set selection for bandlimited graph signals using graph spectral proxies, IEEE Transactions on Signal Processing, 64 (2016), 3775-3789.
doi: 10.1109/TSP.2016.2546233. |
[4] |
A. Anis, A. E. Gamal, S. Avestimehr and A. Ortega, Asymptotic justification of bandlimited interpolation of graph signals for semi-supervised learning, in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2015, 5461–5465.
doi: 10.1109/ICASSP.2015.7179015. |
[5] |
E. Arias-Castro, Clustering based on pairwise distances when the data is of mixed dimensions, IEEE Transactions on Information Theory, 57 (2011), 1692–1706.
doi: 10.1109/TIT.2011.2104630. |
[6] |
E. Arias-Castro, G. Lerman and T. Zhang,
Spectral clustering based on local PCA, Journal of Machine Learning Research, 18 (2017), 1-57.
|
[7] |
F. Aurenhammer,
Voronoi diagrams—a survey of a fundamental geometric data structure, ACM Computing Surveys (CSUR), 23 (1991), 345-405.
doi: 10.1145/116873.116880. |
[8] |
M.-F. Balcan, A. Beygelzimer and J. Langford,
Agnostic active learning, Journal of Computer and System Sciences, 75 (2009), 78-89.
doi: 10.1016/j.jcss.2008.07.003. |
[9] |
M.-F. Balcan, A. Broder and T. Zhang, Margin based active learning, in International Conference on Computational Learning Theory, Springer, 4359 (2007), 35–50.
doi: 10.1007/978-3-540-72927-3_5. |
[10] |
A. Beygelzimer, S. Kakade and J. Langford, Cover trees for nearest neighbor, in Proceedings of the 23rd International Conference on Machine Learning, ACM, 2006, 97–104.
doi: 10.1145/1143844.1143857. |
[11] |
N. Cahill, W. Czaja and D. Messinger, Schroedinger eigenmaps with nondiagonal potentials for spatial-spectral clustering of hyperspectral imagery, in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, vol. 9088, International Society for Optics and Photonics, 2014, 908804. Google Scholar |
[12] |
G. Camps-Valls, T. Marsheva and D. Zhou,
Semi-supervised graph-based hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, 45 (2007), 3044-3054.
doi: 10.1109/TGRS.2007.895416. |
[13] |
C. Cariou and K. Chehdi,
Unsupervised nearest neighbors clustering with application to hyperspectral images, IEEE Journal of Selected Topics in Signal Processing, 9 (2015), 1105-1116.
doi: 10.1109/JSTSP.2015.2413371. |
[14] |
R. Castro and R. Nowak,
Minimax bounds for active learning, IEEE Transactions on Information Theory, 54 (2008), 2339-2353.
doi: 10.1109/TIT.2008.920189. |
[15] |
C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, vol. 1, Springer Science & Business Media, 2003. Google Scholar |
[16] | O. Chapelle, B. Scholkopf and A. Zien, Semi-supervised Learning, MIT Press, 2006. Google Scholar |
[17] |
S. Chen, R. Varma, A. Sandryhaila and J. Kovačević,
Discrete signal processing on graphs: Sampling theory, IEEE Transactions on Signal Processing, 63 (2015), 6510-6523.
doi: 10.1109/TSP.2015.2469645. |
[18] |
Y. Chen, Z. Lin, X. Zhao, G. Wang and Y. Gu,
Deep learning-based classification of hyperspectral data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (2014), 2094-2107.
doi: 10.1109/JSTARS.2014.2329330. |
[19] |
Y. Chen, S. Ma, X. Chen and P. Ghamisi,
Hyperspectral data clustering based on density analysis ensemble, Remote Sensing Letters, 8 (2017), 194-203.
doi: 10.1080/2150704X.2016.1249295. |
[20] |
J. Cohen,
A coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20 (1960), 37-46.
doi: 10.1177/001316446002000104. |
[21] |
D. Cohn, L. Atlas and R. Ladner,
Improving generalization with active learning, Machine Learning, 15 (1994), 201-221.
doi: 10.1007/BF00993277. |
[22] |
R. Coifman and S. Lafon,
Diffusion maps, Applied and Computational Harmonic Analysis, 21 (2006), 5-30.
doi: 10.1016/j.acha.2006.04.006. |
[23] |
R. Coifman, S. Lafon, A. Lee, M. Maggioni, B. Nadler, F. Warner and S. Zucker, Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps, Proceedings of the National Academy of Sciences of the United States of America, 102 (2005), 7426-7431. Google Scholar |
[24] |
S. Dasgupta,
Two faces of active learning, Theoretical Computer Science, 412 (2011), 1767-1781.
doi: 10.1016/j.tcs.2010.12.054. |
[25] |
S. Dasgupta and D. Hsu, Hierarchical sampling for active learning, in Proceedings of the 25th International Conference on Machine Learning, ACM, 2008,208–215.
doi: 10.1145/1390156.1390183. |
[26] |
S. Dasgupta, D. Hsu and C. Monteleoni, A general agnostic active learning algorithm, in Advances in neural information processing systems, 2008,353–360. Google Scholar |
[27] |
A. Esteva, B. Kuprel, R. Novoa, J. Ko, S. Swetter, H. Blau and S. Thrun,
Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542 (2017), 115-118.
doi: 10.1038/nature21056. |
[28] |
J. Friedman, T. Hastie and R. Tibshirani, The Elements of Statistical Learning, vol. 1, Springer series in Statistics Springer, Berlin, 2001.
doi: 10.1007/978-0-387-21606-5. |
[29] |
N. Garcia Trillos, M. Gerlach, M. Hein and D. Slepcev, Error estimates for spectral convergence of the graph Laplacian on random geometric graphs towards the Laplace–Beltrami operator, arXiv: 1801.10108. Google Scholar |
[30] |
N. Garcia Trillos, F. Hoffmann and B. Hosseini, Geometric structure of graph Laplacian embeddings, arXiv: 1901.10651. Google Scholar |
[31] |
M. Gavish and B. Nadler,
Normalized cuts are approximately inverse exit times, SIAM Journal on Matrix Analysis and Applications, 34 (2013), 757-772.
doi: 10.1137/110826928. |
[32] |
N. Gillis, D. Kuang and H. Park,
Hierarchical clustering of hyperspectral images using rank-two nonnegative matrix factorization, IEEE Transactions on Geoscience and Remote Sensing, 53 (2015), 2066-2078.
doi: 10.1109/TGRS.2014.2352857. |
[33] |
J. Ham, Y. Chen, M. Crawford and J. Ghosh,
Investigation of the random forest framework for classification of hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, 43 (2005), 492-501.
doi: 10.1109/TGRS.2004.842481. |
[34] |
S. Hanneke,
Rates of convergence in active learning, The Annals of Statistics, 39 (2011), 333-361.
doi: 10.1214/10-AOS843. |
[35] |
A. Krizhevsky, I. Sutskever and G. Hinton,
Imagenet classification with deep convolutional neural networks, Communications of the ACM, 60 (2017), 84-90.
doi: 10.1145/3065386. |
[36] |
S. Lafon and A. Lee, Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (2006), 1393-1403. Google Scholar |
[37] |
J. Li, J. Bioucas-Dias and A. Plaza,
Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning, IEEE Transactions on Geoscience and Remote Sensing, 48 (2010), 4085-4098.
doi: 10.1109/TGRS.2010.2060550. |
[38] |
J. Li, J. Bioucas-Dias and A. Plaza, Semisupervised hyperspectral image classification using soft sparse multinomial logistic regression, IEEE Geoscience and Remote Sensing Letters, 10 (2013), 318-322. Google Scholar |
[39] |
A. Little, M. Maggioni and J. Murphy, Path-based spectral clustering: Guarantees, robustness to outliers, and fast algorithms, arXiv: 1712.06206. Google Scholar |
[40] |
M. Maggioni and J. Murphy, Learning by unsupervised nonlinear diffusion, arXiv: 1810.06702. Google Scholar |
[41] |
F. Melgani and L. Bruzzone, Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on geoscience and remote sensing, 42 (2004), 1778-1790. Google Scholar |
[42] |
D. Mixon, S. Villar and R. Ward,
Clustering subgaussian mixtures by semidefinite programming, Information and Inference: A Journal of the IMA, 6 (2017), 389-415.
doi: 10.1093/imaiai/iax001. |
[43] |
J. Murphy and M. Maggioni, Iterative active learning with diffusion geometry for hyperspectral images, in 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), IEEE, 2018, 1–5.
doi: 10.1109/WHISPERS.2018.8747033. |
[44] |
J. Murphy and M. Maggioni, Spectral-spatial diffusion geometry for hyperspectral image clustering, arXiv: 1902.05402. Google Scholar |
[45] |
J. Murphy and M. Maggioni,
Unsupervised clustering and active learning of hyperspectral images with nonlinear diffusion, IEEE Transactions on Geoscience and Remote Sensing, 57 (2019), 1829-1845.
doi: 10.1109/TGRS.2018.2869723. |
[46] |
B. Nadler and M. Galun, Fundamental limitations of spectral clustering, in Advances in Neural Information Processing Systems, 2007, 1017–1024. Google Scholar |
[47] |
A. Paoli, F. Melgani and E. Pasolli,
Clustering of hyperspectral images based on multiobjective particle swarm optimization, IEEE Transactions on Geoscience and Remote Sensing, 47 (2009), 4175-4188.
doi: 10.1109/TGRS.2009.2023666. |
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