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.