Clustering-based locally linear embedding

The locally linear embedding (LLE) algorithm is considered as a powerful method for the problem of nonlinear dimensionality reduction. In this paper, first, a new method called clustering-based locally linear embedding (CLLE) is proposed, which is able to solve the problem of high time consuming of LLE and preserve the data topology at the same time. Then, how the proposed method achieves decreasing the time complexity of LLE is analyzed. Moreover, the further comparison shows that CLLE performs better in most cases than LLE on the time cost, topology preservation, and classification performance with several different data sets.