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

• 作者: Fulin Luo Hailiang Feng Hong Huang Zezhong Ma
• 来源期刊: 电脑和通信(英文)
• 学科: 工学
• 关键词: HYPERSPECTRAL Image Classification Dimensionality REDUCTION Multiple MANIFOLDS Structure SPARSE REPRESENTATION SEMI-SUPERVISED Learning
• 年，卷（期）: 2015,（11）
• 页码范围: 33-39
• 页数: 7页
• 分类号: TP39

摘要

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.