A modified local linear embedding algorithm based on neighbour selection. (2015) - Liu, ShiYao; Tang, Tu; Kang, Qi; Wu, QiDi
Dimension reduction plays an important role for effectively extracting useful information from data in practical solutions. Locally Linear Embedding (LLE) is a promising non-linear dimensionality reduction method. However, LLE has some limitations in dealing with the problem of uneven distribution of data, i.e. the number of neighbours influences the size of local region. To solve this problem, this paper proposes a modified LLE method named LLE+, through improving the similarity measure for neighbour selection. The experiments proved that LLE+ has a better dimension reduction performance.