关键词: 非线性流形学习, 降维, 局部线性嵌入

Abstract: Nonlinear manifold learning methods for dimensionality reduction are widely applied to face recognition, intrusion detection and sensor networks, etc. However, few manifold learning algorithms can deal effectively with sparse data. This paper proposes a sparse embedding algorithm based on conceptual framework of Local Linear Embedding algorithm（LLE）, which can achieve the purpose of extensive overlapping of information by expanding and strengthening local neighborhood information in the case of sparse samples. The experimental results on sparse artificial and face datasets show that the proposed algorithm generates a better embedding and classification result.

Key words: nonlinear manifold learning, dimensionality reduction, Local Linear Embedding（LLE）