Dimensionality Reduction

concepts · 14 notes linked

Related: Principal Component Analysis · Singular Value Decomposition · Linear Algebra · Numpy · Scikit Learn · Correspondence Analysis · Deepmind · Game Theory

Notes

• #009 The Singular Value Decomposition(SVD) - illustrated in Python - Master Data Science — Step-by-step SVD tutorial with Python implementation and image reconstruction examples
• Allen Tran — Probabilistic PCA with EM handles missing data in macroeconomic series
• Detrended correspondence analysis - Wikipedia — Ordination method correcting arch and edge artifacts in ecological gradient data
• Diffusion wavelets - Wikipedia — Adaptive multiscale wavelet basis built from diffusion operator on graphs
• Game theory as an engine for large-scale data analysis — EigenGame reformulates PCA as competitive multi-agent Nash equilibrium
• Isomap - Wikipedia — Nonlinear dimensionality reduction using geodesic distances on manifold neighborhood graph
• Kosambi–Karhunen–Loève theorem - Wikipedia — Optimal orthogonal decomposition of stochastic processes minimizing mean squared error
• Making sense of principal component analysis, eigenvectors & eigenvalues — Geometric and computational walkthrough of PCA via eigendecomposition and SVD
• Multiple correspondence analysis - Wikipedia — PCA counterpart for nominal categorical data using chi-square distances
• Principal Component Analysis with numpy — Implementing PCA via numpy covariance matrix and eigendecomposition
• Simple Movie Recommender Using SVD — SVD-based movie recommender using cosine similarity on principal components
• Simple and Deterministic Matrix Sketches — Streaming deterministic algorithm for matrix sketching with 1/ell error bound
• Singular Value Decomposition Part 1: Perspectives on Linear Algebra — Intuitive motivation for SVD as data approximation via matrix factorization
• scikit-learn — Python machine learning library covering classification through preprocessing