Methodogies focus on unsupervised learning techniques. Topics include:
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(i) linear and non-linear dimensionality reduction
- principal component analysis (PCA)
- canonical correlation analysis (CCA)
- multidimensional scaling (MDS)
- Kernel PCA
- local linear embedding (LLE)
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(ii) clustering methods
- K-means clustering
- hierarchical clustering
- spectral clustering
- DBSCAN
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(iii) graph-based learning (Isomap)