An Iterative Locally Linear Embedding Algorithm
By: Researcher II
Disclaimer: “The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).”
Please contact us in case of a broken link from here
| Authors | Deguang Kong, Chris Ding, Heng Huang, Feiping Nie |
| Journal/Conference Name | The 29th International Conference on Machine Learning (ICML) |
| Paper Category | Computer Science |
| Paper 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. |
| Date of publication | 2012 |
| Code Programming Language | MATLAB |
| Comment |
Copyright Researcher II 2022