An Iterative Locally Linear Embedding Algorithm
Machine Learning
2012-07-03 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.1206.6463,
title = {An Iterative Locally Linear Embedding Algorithm},
author = {Deguang Kong and Chris H. Q. Ding and Heng Huang and Feiping Nie},
journal= {arXiv preprint arXiv:1206.6463},
year = {2012}
}
Comments
Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)