Adaptive Neighborhood Selection Method Based on Silhouette Index for Locally Linear Embedding
Quan Jiang, Peng Huang, Huarong Li · International Journal of Advancements in Computing Technology · 2013
Abstract Locally linear embedding (LLE) is a highly popular manifold learning and nonlinear dimensionality reduction technique. However, the neighborhood parameters of the algorithm are sensitive to the mapping results and difficult to choose. In this paper, we propose an adaptive neighborhood selection method based on Silhouette index for LLE algorithm. From the point of the cluster quality of feature extraction, Silhouette index is applied to evaluate the cluster effect of the low-dimensional embedding structure, in order to determine the optimal neighborhood parameters of LLE algorithm. Applied to IRIS and compressor failure data, the new method can effectively extract the inherent characteristics of low-dimensional manifolds, and obviously improve the classification performance of pattern recognition. The experimental results validate the feasibility and effectiveness of the proposed method.