(→Outer Characteristics of the point cloud Methods) |
|||
| Line 10: | Line 10: | ||
* [[PCA: Principal Component Analysis]] | * [[PCA: Principal Component Analysis]] | ||
| − | * [[Fisher | + | * [[Fisher Linear Discriminant_OldKiwi]] |
== Inner characteristics of the point cloud Methods == | == Inner characteristics of the point cloud Methods == | ||
* [[MDS_OldKiwi]] | * [[MDS_OldKiwi]] | ||
Revision as of 00:47, 18 April 2008
Consider a collection of sample points $ \{x_1,x_2,\cdots,x_n\} $ where $ x_i \in R^m $. We divide the methods in two categories:
- Outer Characteristics of the point cloud: These methods require the spectral analysis of a positive definite kernel of dimension m, the extrinsic dimensionality of the data.
- Inner characteristics of the point cloud: These methods require the spectral analysis of a positive definite kernel of dimension n, the number of samples in the sample cloud.
