Dimensionality Reduction Algorithm Based on Density Portrayal

In order to improve the correctness of dimensionality reduction algorithms based on Locally Linear Embedding(LLE) caused by data density change,a novel approach based on density is proposed in this paper.It adapts cam distribute to find the data’s nearest neighbor,meanwhile,adds the data’s density information during the low dimensional local reconstruction.The proposed algorithm is used to reduce the dimensionality of input feature,and the reduced feature is classified by simple classifier.Experimental result indicates that the method can effectively improve the recognition rate of handwritten digits and can dig the manifold embedded in the high dimensional space.