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Shao, Z. and Zhang, L. (2014) Sparse Dimensionality Reduction of Hyperspectral Image Based on Semi-Supervised Local Fisher Discriminant Analysis. International Journal of Applied Earth Observation and Geoinformation, 31, 122-129. http://dx.doi.org/10.1016/j.jag.2014.03.015

has been cited by the following article:

• TITLE: Semi-Supervised Dimensionality Reduction of Hyperspectral Image Based on Sparse Multi-Manifold Learning

  AUTHORS: Hong Huang, Fulin Luo, Zezhong Ma, Hailiang Feng

  KEYWORDS: Hyperspectral Image Classification; Dimensionality Reduction, Multiple Manifolds Structure, Sparse Representation, Semi-Supervised Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.3 No.11, November 19, 2015

  ABSTRACT: In this paper, we proposed a new semi-supervised multi-manifold learning method, called semi- supervised sparse multi-manifold embedding (S3MME), for dimensionality reduction of hyperspectral image data. S3MME exploits both the labeled and unlabeled data to adaptively find neighbors of each sample from the same manifold by using an optimization program based on sparse representation, and naturally gives relative importance to the labeled ones through a graph-based methodology. Then it tries to extract discriminative features on each manifold such that the data points in the same manifold become closer. The effectiveness of the proposed multi-manifold learning algorithm is demonstrated and compared through experiments on a real hyperspectral images.