@inproceedings{ec8e778b32aa498995bac4a6f3d17641,
title = "An iterative locally linear embedding algorithm",
abstract = "Locally Linear embedding (LLE) is a popular dimension reduction method. In this paper, we systematically improve the two main steps of LLE: (A) learning the graph weights W, and (B) learning the embedding Y. We propose a sparse nonnegative W learning algorithm. We propose a weighted formulation for learning Y and show the results are identical to normalized cuts spectral clustering. We further propose to iterate the two steps in LLE repeatedly to improve the results. Extensive experiment results show that iterative LLE algorithm significantly improves both classification and clustering results.",
author = "Deguang Kong and Chris Ding and Heng Huang and Feiping Nie",
year = "2012",
language = "英语",
isbn = "9781450312851",
series = "Proceedings of the 29th International Conference on Machine Learning, ICML 2012",
pages = "1647--1654",
booktitle = "Proceedings of the 29th International Conference on Machine Learning, ICML 2012",
note = "29th International Conference on Machine Learning, ICML 2012 ; Conference date: 26-06-2012 Through 01-07-2012",
}