Local linear embedding algorithm based on tightness of data points

Locally Linear Embedding(LLE)algorithm is one of the nonlinear data dimensionality reduction approaches based on manifold learning. Considering the distribution of data points mostly present the heterogeneity, there will result in large amounts ofinformation loss when LLE selects neighboring points. This paper proposes a novel locally linear embedding algorithm based on tightness of data points, named tLLE, which can reduce dimensionality effectively for the datasets that present the non-uniform distribution. And, it has better effects of dimensionality reduction than LLE. The embedding and classification results on synthetic and real data show that tLLE is very effective.