1 / 20

Gemoetrically local embedding in manifolds for dimension reduction

Gemoetrically local embedding in manifolds for dimension reduction. Presenter : Kung, Chien-Hao Authors : Shuzhi Sam Ge , Hongsheng He, Chengyao Shen 2012,PR. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

Download Presentation

Gemoetrically local embedding in manifolds for dimension reduction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Gemoetrically local embedding in manifolds for dimension reduction Presenter : Kung, Chien-HaoAuthors : Shuzhi Sam Ge, Hongsheng He, ChengyaoShen2012,PR

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • LLE is a dimension reduction technique which preserve neighborhood relationships amongst data. • However, Euclidean distance is limited as only the pairwise distance to the target data is considered.

  4. Objectives • This paper uses geometry distance which emphasized the local geometrical structure of the manifold spanned instead of computing the pairwise metric between data.

  5. Methodology-Framework Geometrical distance construction Optimal reconstruction Outlier-suppressingembedding

  6. Methodology Neighbor selection using geometry distances Tikhonov regularization

  7. Methodology Alternative neighbor selection

  8. Methodology Linear embedding

  9. Methodology Outlier data filtering

  10. Experiment

  11. Experiment

  12. Experiment

  13. Experiment

  14. Experiment

  15. Experiment

  16. Experiment

  17. Experiment

  18. Experiment

  19. Conclusions • The GLE algorithm performs well in extracting inner structures of input linear manifold with outliers. • The GLE behaves as a clustering and classification method by projecting the feature data into low-dimensional separable regions. • The major drawback of GLE is the slow computation speed compared with other algorithms when the input data is small.

  20. Comments • Advantages • This paper supplies the completely formula information. But this paper is hard to understand when the reader is a lack of prior knowledge. • Applications • Dimension reduction.

More Related