Lecture 7: Unsupervised Learning Techniques
Andrea Caponnetto
Description
To introduce some methods for unsupervised learning:
Gaussian Mixtures, K-Means, ISOMAP, HLLE, Laplacian Eigenmaps.
Slides
Slides for this lecture: PS, PDF
Suggested Reading
- Hastie, Tibshirani, Friedman. The Elements of
Statistical Learning: Data Mining, Inference, and Prediction.
Springer 2001.
- Tenenbaum, de Silva, Langford.
A Global Geometric Framework for Nonlinear Dimensionality Reduction.
Science 22 December 2000; Vol. 290. no. 5500, pp. 2319 - 2323.
- Donoho, Grimes.
Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. 2003.
- M. Belkin, P. Niyogi.
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation.
Neural Computation, June 2003; 15 (6):1373-1396.