This lecture covers the problem of dimensionality reduction - one of the fundamental problems in statistics and machine learning. The aim is to give a good intuition of the problem from several different perspectives, discuss potential applications and explain fundamentals of basic and more advanced models. The lecture covers the following topics:
- Linear Mapping to Embedded Space
- Principal Component Analysis
- Probabilistic PCA
- Non-linear Dimensionality Reduction
- Gaussian Process Latent Variable Models
LectureThe lecture slides are available for download below:
Lecture NotesThe following lecture notes provide some of the derivations for the material discussed during the lecture.
Dimensionality ReductionThe following books and papers are a great source of knowledge supplementing the lectures:
- Pattern Recognition and Machine Learning, C.Bishop, Springer, 2007.
- Neil D. Lawrence: Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models. Journal of Machine Learning Research 6: 1783-1816 (2005)
Additional Online MaterialsMatlab
- Introductory materials
- Reference manuals
- Matlab Documentation - Programming Fundamentals
- Matlab Documentation - Mathematics
- Matlab Documentation - Data Import and Export
- Matlab Documentation - Desktop Tools and Development Environment
- Matlab Documentation - Graphics
- Matlab Documentation - 3D Visualization
- Complete Matlab Online Documentation
- StackOverflow Matlab - A great place to ask questions and find solutions to problems with Matlab.