7. DIMENSIONALITY REDUCTION (CONTEXT-DEPENDENT - Seventh Priority) (1/5) ├── Linear Methods (3/5) │ ├── Principal Component Analysis (PCA) (4/5) │ │ ├── sklearn.decomposition.PCA │ │ └── ✓ Transforms data to orthogonal components, retains variance │ ├── Linear Discriminant Analysis (LDA) (3/5) │ │ ├── sklearn.discriminant_analysis.LinearDiscriminantAnalysis │ │ └── ✓ Maximizes class separability, supervised │ ├── Independent Component Analysis (ICA) (2/5) │ │ ├── sklearn.decomposition.FastICA │ │ └── ✓ Separates multivariate signal into independent components │ └── Factor Analysis (2/5) │ ├── sklearn.decomposition.FactorAnalysis │ └── ✓ Explains variance using a smaller number of latent factors │ ├── Non-Linear Methods (3/5) │ ├── t-Distributed Stochastic Neighbor Embedding (t-SNE) (4/5) │ │ ├── sklearn.manifold.TSNE │ │ └── ✓ Best for visualization, preserves local structure │ ├── UMAP (Uniform Manifold Approximation and Projection) (4/5) │ │ ├── umap-learn.UMAP │ │ └── ✓ Faster than t-SNE, good for visualization and general embedding │ ├── Kernel PCA (2/5) │ │ ├── sklearn.decomposition.KernelPCA │ │ └── ✓ Non-linear PCA using kernel tricks │ └── Autoencoders (3/5) │ ├── tensorflow.keras.models.Sequential │ ├── torch.nn.Module │ └── ✓ Neural network for learning compressed data representation │ └── Sparse Methods (2/5) ├── Sparse PCA (2/5) │ ├── sklearn.decomposition.SparsePCA │ └── ✓ PCA with sparse components, improves interpretability └── Dictionary Learning (2/5) ├── sklearn.decomposition.DictionaryLearning └── ✓ Learns a dictionary of sparse components