1. Introduction
2. Methods based on Statistics and Information Theory
2.1 Vector Quantization and Mixture models
2.2 PCA
2.3 Principal curves, surfaces and manifolds
2.4 Generative Topographic Mapping
2.5 Self-Organizing Maps
2.6 Elastic maps, nets, principal graphs and principal trees
2.7 Kernel PCA and Multidimensional Scaling
2.8 Kernel Entropy Component Analysis
2.9 Robust PCA
2.10 Factor Analysis
2.11 Independent Component Analysis
3. Methods based on Dictionaries
3.1 Non-negative Matrix Factorization
3.2 Principal Tensor Analysis and Non-negative Tensor Factorization
3.3 Generalized SVD
3.4 Sparse representations and overcomplete dictionaries
4. Methods based on projections
4.1 Projection onto interesting directions
4.2 Projection onto manifolds
5. Trends and Conclusions