Abstract
Manifold learning algorithms have been demonstrated to be effective for hyperspectral data dimension reduction (DR). However, the low dimensional feature representation resulted by traditional manifold learning algorithms could not preserve the nonnegative property of the hyperspectral data, which leads inconsistency with the psychological intuition of “combining parts to form a whole”. In this paper, we introduce a nonnegative discriminative manifold learning (NDML) algorithm for hyperspectral data DR, which yields a discriminative and low dimensional feature representation, with psychological and physical evidence in the human brain. Our method benefits from both the nonnegative matrix factorization (NMF) algorithm and the discriminative manifold learning (DML) algorithm. We apply the NDML algorithm to hyperspectral remote sensing image classification on HYDICE dataset. Experimental results confirm the efficiency of the proposed NDML algorithm, compared with some existing manifold learning based DR methods.
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Zhang, L., Zhang, L., Tao, D., Huang, X., Du, B. (2013). Nonnegative Discriminative Manifold Learning for Hyperspectral Data Dimension Reduction. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_45
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DOI: https://doi.org/10.1007/978-3-642-42057-3_45
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