| Line 1: | Line 1: | ||
| − | Consider a collection of sample points <math>\{x_1,x_2,\cdots,x_n\}</math> where <math> x_i \ | + | Consider a collection of sample points <math>\{x_1,x_2,\cdots,x_n\}</math> where <math> x_i \in R^m</math>. 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. | ||
| + | |||
| + | * PCA: Principal Component Analysis | ||
| + | |||
| + | * Fisher Discriminant Analysis | ||
| + | |||
| + | == 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. | ||
| + | |||
| + | * MDS | ||
Revision as of 23:39, 17 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.
- PCA: Principal Component Analysis
- Fisher Discriminant Analysis
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.
- MDS
