A Summary of Unsupervised Learning Methods

This chapter summarizes the relationships and characteristics of eight commonly used machine learning methods in detail, namely clustering methods (including hierarchical clustering and k-means clustering), Singular Value Decomposition (SVD), Principal Component Analysis (PCA), Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), Markov Chain Monte Carlo method (MCMC, including Metropolis–Hastings algorithm and Gibbs sampling), Latent Dirichlet Allocation (LDA), and PageRank algorithm.