PCA

Data compression technique

PCA stands for principal component analysis, a statistical technique that reduces complex data to its most important dimensions.

If you have measurements on dozens of variables, PCA finds the handful of underlying factors that explain most of the variation: The PCA revealed that three components accounted for 85% of the variance.

Widely used in data science, genetics, and image processing to simplify high-dimensional datasets while preserving essential patterns.

technique

principal

Chief school administrator

componentsConstituent parts
analysisMethodical study of parts
dimensionalityNumber of independent directions

reduction · eigenvalues · eigenvectors · covariance · matrix · decomposition · transformation · projection · orthogonal · feature

variance

explained

Made something understandable

cumulativeIncreasing by successive additions
proportionComparative relation of parts
percentageProportion out of hundred

retained · total · threshold · cutoff · scree · eigenvalues · spectrum · inertia · contribution · reconstruction

applications

compression

Forcing into smaller space

visualizationForming mental or graphical images
denoisingRemoving unwanted signal artifacts
preprocessingInitial data preparation steps

face · recognition · genomics · finance · image · processing · clustering · regression · anomaly · detection

Opposites

dimensionality approach

PCAraw data

Unanalyzed collected information

upsampling · feature engineering

analytical method type

PCAclustering

Forming concentrated groups

k-means · classification · segmentation · grouping · partitioning

data analysis philosophy

PCAdeep learning

Layered neural network methods

Familiarity: Top 1,000,000
Profile: Used worldwide · Timeless · Abstract · Rated G