An Iterative Locally Linear Embedding Algorithm
This work addresses a domain-specific problem in machine learning for researchers and practitioners using dimension reduction techniques, presenting incremental improvements to an existing method.
The paper tackled the problem of improving Local Linear Embedding (LLE) for dimension reduction by proposing an iterative algorithm that combines nonnegative constraints, repeated iteration of steps, and relaxed kNN constraints, resulting in significant improvements in classification and clustering results as shown in extensive experiments.
Local Linear embedding (LLE) is a popular dimension reduction method. In this paper, we first show LLE with nonnegative constraint is equivalent to the widely used Laplacian embedding. We further propose to iterate the two steps in LLE repeatedly to improve the results. Thirdly, we relax the kNN constraint of LLE and present a sparse similarity learning algorithm. The final Iterative LLE combines these three improvements. Extensive experiment results show that iterative LLE algorithm significantly improve both classification and clustering results.