2. Unsupervised learning 2.1.1. Gaussian Mixture 2.2. Manifold learning 2.2.2. Isomap2.2.4. Modified Locally Linear Embedding2.2.6. Spectral Embedding2.2.8. Multi-dimensional Scaling (MDS)2.2.10. Tips on practical use 2.3.1. Overview of clustering methods2.3.4. Mean Shift2.3.6. Hierarchical clustering2.3.8. OPTICS2.3.10. Clustering performance evaluation 2.4.1. Spectral Co-Clustering2.4.3. Biclustering evaluation 2.5.1. Principal component analysis (PCA)2.5.3. Dictionary Learning2.5.6. Non-negative matrix factorization (NMF or NNMF) 2.6. Covariance estimation 2.6.2. Shrunk Covariance2.6.4. Robust Covariance Estimation 2.7.1. Overview of outlier detection methods2.7.3. Outlier Detection 2.8. Density Estimation 2.8.2. Kernel Density Estimation