heucoder/dimensionality_reduction_alo_codes resource

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2370 stars · 609 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Python demo implementations of classic dimensionality reduction algorithms including PCA, KPCA, LDA, MDS, ISOMAP, LLE, TSNE, AutoEncoder, FastICA, SVD, LE, and LPP. Each algorithm comes with example code, visualization results, and links to reference tutorials.

Use cases

• learn how PCA works with python code
• implement t-SNE from scratch for visualization
• compare dimensionality reduction algorithms
• understand LLE and ISOMAP manifold learning
• find reference implementations of LDA and SVD
• study feature extraction and data reduction techniques

When to choose

• learning or teaching dimensionality reduction algorithms
• needing simple, readable reference implementations with visualizations
• exploring which reduction algorithm suits your data

When to avoid

• production pipelines needing optimized, well-tested libraries like scikit-learn
• needing GPU-accelerated or scalable implementations for large datasets

Facets

learning-resource · maturity maintenance

machine-learning data-science data-visualization machine-learning data-science tutorials python dimensionality-reduction pca lda tsne feature-extraction demo-code educational algorithms

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