Machine Learning Lesson of the Day: Clustering, Density Estimation and Dimensionality Reduction
January 6, 2014 6 Comments
Nonetheless, here are some categories of unsupervised learning that cover many of its commonly used techniques. I learned this categorization from Mathematical Monk, who posted a great set of videos on machine learning on Youtube.
- Clustering: Categorize the observed variables into groups that maximize some similarity criterion, or, equivalently, minimize some dissimilarity criterion.
- Example: K-Means Clustering
- Density Estimation: Use statistical models to find an underlying probability distribution that gives rise to the observed variables.
- Dimensionality Reduction: Find a smaller set of variables that captures the essential variations or patterns of the observed variables. This smaller set of variables may be just a subset of the observed variables, or it may be a set of new variables that better capture the underlying variation of the observed variables.
- Example: Principal component analysis
Are there any other categories that you can think of? How would you categorize hidden Markov models? Your input is welcomed and appreciated in the comments!