• Acta Photonica Sinica
  • Vol. 43, Issue 6, 630002 (2014)
WEI Feng*, HE Mingyi, SHEN Zhiming, and LI Xu
Author Affiliations
  • [in Chinese]
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    DOI: 10.3788/gzxb20144306.0630002 Cite this Article
    WEI Feng, HE Mingyi, SHEN Zhiming, LI Xu. Manifold based Semi supervised Feature Selection for Hyperspectral Data[J]. Acta Photonica Sinica, 2014, 43(6): 630002 Copy Citation Text show less
    References

    [1] PLAZA A, BENEDIKTSSON J A, BOARDMAN J W, et al. Recent advances in techniques for hyperspectral image processing [J]. Remote Sensing of Environment, 2009, 113: S110-S122.

    [2] JIA X, KUO B C, CRAWFORD M M. Feature mining for hyperspectral image classification [J]. Proceedings of the IEEE, 2013, 101(3): 676-697.

    [3] RODARMEL C, SHAN J. Principal component analysis for hyperspectral image classification [J]. Surveying and Land Information Science, 2002, 62(2): 115-122.

    [4] ETEMAD K, CHELLAPPA R. Discriminant analysis for recognition of human face images [J]. JOSA A, 1997, 14(8): 1724-1733.

    [5] HE X, NIYOGI P. Locality preserving projections [C]. Neural Information Processing Systems, 2003, 16: 234-241.

    [6] CAMPSVALLS G, BANDOS M T, ZHOU D. Semisupervised graphbased hyperspectral image classification [J]. Geoscience and Remote Sensing, IEEE Transactions on, 2007, 45(10): 3044-3054.

    [7] YANG L X, YANG S Y, JIN P L, et al. Semisupervised hyperspectral image classification using spatiospectral laplacian support vector machine [J]. Geoscience and Remote Sensing Letters IEEE, 2014, 11(3): 651-655.

    [8] ZHAO J, LU K, HE X. Locality sensitive semisupervised feature selection [J]. Neurocomputing, 2008, 71(10): 1842-1849.

    [9] PAL M, FOODY G M. Feature selection for classification of hyperspectral data by SVM [J]. Geoscience and Remote Sensing, IEEE Transactions on, 2010, 48(5): 2297-2307.

    [10] BELKIN M, NIYOGI P, SINDHWANI V. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples [J]. The Journal of Machine Learning Research, 2006, 7: 2399-2434.

    [11] HE X, CAI D, NIYOGI P. Laplacian score for feature selection [C]. Advances in Neural Information Processing Systems, 2005: 507-514.

    [12] YANG J, ZHANG D, JIN Z, et al. Unsupervised discriminant projection analysis for feature extr [C]. International Conference on Pattern Recognition. IEEE, 2006, 1: 904-907.

    [13] BACHMANN C M, AINSWORTH T L, FUSINA R A. Exploiting manifold geometry in hyperspectral imagery [J]. Geoscience and Remote Sensing, IEEE Transactions on, 2005, 43(3): 441-454.

    [14] BELKIN M, NIYOGI P. Laplacian eigenmaps for dimensionality reduction and data representation [J]. Neural Computation, 2003, 15(6): 1373-1396.

    [15] CHUNG F R K. Spectral graph theory [M]. Fresno: AMS Bookstore, 1997.

    [16] SUGIYAMA M. Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis [J]. The Journal of Machine Learning Research, 2007, 8: 1027-1061.

    [17] DUDA R O, HART P E, STORK D G. Pattern classification [M]. New York: John Wiley & Sons, 2012.

    CLP Journals

    [1] HUANG Hong, YANG Ya-qiong, LUO Fu-lin, FENG Hai-liang. Classification of Hyperspectral Remote Sensing Images Based on Supervised Sparse Manifold Embedding[J]. Acta Photonica Sinica, 2015, 44(12): 1228001

    WEI Feng, HE Mingyi, SHEN Zhiming, LI Xu. Manifold based Semi supervised Feature Selection for Hyperspectral Data[J]. Acta Photonica Sinica, 2014, 43(6): 630002
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