Principal Component Analysis (PCA)

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• AI Solver
• ...find outliers
• Clustering
• Manifold Hypothesis
• Anomaly Detection
• Dimensional Reduction
• Unsupervised Learning
• Embedding
  • T-Distributed Stochastic Neighbor Embedding (t-SNE) ..non-linear
• How to Calculate Principal Component Analysis (PCA) from Scratch in Python | Jason Brownlee - Machine Learning Mastery
• Data Science Concepts Explained to a Five-year-old | Megan Dibble - Toward Data Science
• Causation vs. Correlation - Multivariate Additive Noise Model (MANM)
  • Independent Component Analysis (ICA) | University of Helsinki
  • Linear Non-Gaussian Acyclic Model (ICA-LiNGAM) | S. Shimizu, P. Hoyer, A. Hyvarinen, and A. Kerminen - University of Helsinki
  • Greedy DAG Search (GDS) | Alain Hauser and Peter Biihlmann
  • Feature-to-Feature Regression for a Two-Step Conditional Independence Test | Q. Zhang, S. Filippi, S. Flaxman, and D. Sejdinovic
• A Beginner's Guide to Eigenvectors, Eigenvalues, PCA, Covariance and Entropy Learning | Chris Nicholson - A.I. Wiki pathmind
• Everything you did and didn't know about PCA | Alex Williams - Its Neutonal

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a data reduction technique that allows to simplify multidimensional data sets to 2 or 3 dimensions for plotting purposes and visual variance analysis.

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1. Center (and standardize) data
2. First principal component axis
   1. Across centroid of data cloud
   2. Distance of each point to that line is minimized, so that it crosses the maximum variation of the data cloud
3. Second principal component axis
   1. Orthogonal to first principal component
   2. Along maximum variation in the data
4. First PCA axis becomes x-axis and second PCA axis y-axis
5. Continue process until the necessary number of principal components is obtained