## Exam
- Start: **Karhunen-Loeve-Transformation (PCA)**
	- Basis rotation / Decomposition of the covariance matrix
	- Dimension Reduction by removing small eigenvalues (variances)
	- Requires a Euclidean space
- **Edit distances**
	- Some data has no Euclidean space
	- Expensive, use GEMINI
	- Prune data and then compute the distance
	- Levenshtein distance
		- Computation
		- Dynamic programming
		- What metrics work? --> Additive metrics (comparable to Markov property; the way we got to the result does not matter)
	- Indexing using M-Trees
- **M-Trees**
	- Idea
	- Spherical geometry using the Triangle Inequality
	- Explain pruning criterion