Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Regularization parameter choice in locally linear embedding
Neurocomputing
Locally linear embedding: a survey
Artificial Intelligence Review
Global and local choice of the number of nearest neighbors in locally linear embedding
Pattern Recognition Letters
Dimensionality reduction-based spoken emotion recognition
Multimedia Tools and Applications
Visualizing dimensionality reduction of systems biology data
Data Mining and Knowledge Discovery
Hi-index | 0.02 |
Locally linear embedding (LLE) is a method for nonlinear dimensionality reduction, which calculates a low dimensional embedding with the property that nearby points in the high dimensional space remain nearby and similarly co-located with respect to one another in the low dimensional space [1]. LLE algorithm needs to set up a free parameter, the number of nearest neighbors k . This parameter has a strong influence in the transformation. In this paper is proposed a cost function that quantifies the quality of the embedding results and computes an appropriate k . Quality measure is tested on artificial and real-world data sets, which allow us to visually confirm whether the embedding was correctly calculated.