Difference between revisions of "Principal Component Analysis (PCA)"
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* [[...find outliers]] | * [[...find outliers]] | ||
* [[Clustering]] | * [[Clustering]] | ||
| + | * [[Manifold Hypothesis]] | ||
* [[Anomaly Detection]] | * [[Anomaly Detection]] | ||
* [[Dimensional Reduction]] | * [[Dimensional Reduction]] | ||
Revision as of 09:12, 3 September 2020
YouTube search... ...Google search
- AI Solver
- ...find outliers
- Clustering
- Manifold Hypothesis
- Anomaly Detection
- Dimensional Reduction
- Unsupervised Learning
- 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
a data reduction technique that allows to simplify multidimensional data sets to 2 or 3 dimensions for plotting purposes and visual variance analysis.
- Center (and standardize) data
- First principal component axis
- Across centroid of data cloud
- Distance of each point to that line is minimized, so that it crosses the maximum variation of the data cloud
- Second principal component axis
- Orthogonal to first principal component
- Along maximum variation in the data
- First PCA axis becomes x-axis and second PCA axis y-axis
- Continue process until the necessary number of principal components is obtained
NumXL
