Unsupervised Learning
- implicit learning
- indirect feedback
- self-organizing (no labeled data)
Clustering
Clustering
Identify similar samples and assign them with a label.
- k-means Clustering
- Gaussian Mixture Expectation Maximization
- Spectral Clustering
- Clustering mit Kruskal
Dimensionality Reduction
Curse of Dimensionality → grows exponentaly.
Typical approaches:
- Feature Selection
- Feature Extraction (combine mutliple features into one) → Feature Engineering
For example: