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
