关键词: 局部线性嵌入（LLE）, 多流形学习, 最佳维度, 分类

Abstract: Manifold learning has attracted extensive interests of researchers from machine learning and data mining. Such as, Locally Linear Embedding（LLE） algorithm has a good generalization performance as a nonlinear dimensionality reduction algorithm, and be wildly used in image classification and object recognition, but just assumes the data resides on a single manifold. Multiple Manifold Locally Linear Embedding（MM-LLE） algorithm as an improved version of considered multi-manifold, there are some shortcomings still. Therefore, an improved MM-LLE algorithm is proposed, which uses the local low dimensional manifolds between any two classes to construct classifiers to improve the classification accuracy, and the way of calculating the best dimension of the original algorithm is improved. This paper compares with algorithm ISOMAP, LLE and MM-LLE in classification accuracy. The experimental results verify the effectiveness of the proposed method.

Key words: Local Linear Embedding（LLE）, multi-manifold learning, best dimension, classification