Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6864882 | Neurocomputing | 2018 | 30 Pages |
Abstract
Locally Linear Embedding (LLE) is widely used for embedding data on a nonlinear manifold. It aims to preserve the local neighborhood structure on the data manifold. Our work begins with a new observation that LLE has a natural robustness property. Motivated by this observation, we propose to integrate LLE and PCA into a LLE guided PCA model (LLE-PCA) that incorporates both global structure and local neighborhood structure simultaneously while performs robustly to outliers. LLE-PCA has a compact closed-form solution and can be efficiently computed. Extensive experiments on five datasets show promising results on data reconstruction and improvement on data clustering and semi-supervised learning tasks.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Bo Jiang, Chris Ding, Bin Luo,