PCA

E97073

dimensionality reduction technique machine learning algorithm scikit-learn transformer unsupervised learning method

PCA (Principal Component Analysis) in scikit-learn is a dimensionality reduction technique that transforms high-dimensional data into a smaller set of uncorrelated components capturing the most variance.

Try in SPARQL Jump to: Surface forms Statements Referenced by

All labels observed (3)

Label	Occurrences
principal component analysis	2
PCA canonical	1
Principal Component Analysis	1

Statements (50)

Predicate	Object
instanceOf	dimensionality reduction technique ⓘ machine learning algorithm ⓘ scikit-learn transformer ⓘ unsupervised learning method ⓘ
assumes	linear relationships in data ⓘ
basedOn	PCA self-linksurface differs ⓘ surface form: Principal Component Analysis
captures	maximum variance directions ⓘ
commonlyUsedFor	data visualization ⓘ feature extraction ⓘ noise reduction ⓘ
compatibleWith	scikit-learn ⓘ surface form: scikit-learn Pipeline
hasAttribute	components_ ⓘ explained_variance_ ⓘ explained_variance_ratio_ ⓘ mean_ ⓘ n_components_ ⓘ n_features_in_ ⓘ noise_variance_ ⓘ singular_values_ ⓘ
implementedIn	Python ⓘ
inputShape	(n_samples, n_features) ⓘ
learnsFrom	covariance structure of the data ⓘ
modulePath	sklearn.decomposition.PCA ⓘ
outputShape	(n_samples, n_components) ⓘ
partOfLibrary	scikit-learn ⓘ
primaryGoal	dimensionality reduction ⓘ variance maximization ⓘ
produces	uncorrelated components ⓘ
requires	numeric input data ⓘ
supportsMethod	fit ⓘ fit_transform ⓘ get_params ⓘ inverse_transform ⓘ set_params ⓘ transform ⓘ
supportsParameter	copy ⓘ dtype ⓘ iterated_power ⓘ n_components ⓘ random_state ⓘ svd_solver ⓘ tol ⓘ whiten ⓘ
svd_solverOption	arpack ⓘ auto ⓘ full ⓘ randomized ⓘ
transforms	high-dimensional data ⓘ
uses	linear transformation ⓘ
whitenEffect	scales components to unit variance ⓘ

Referenced by (4)

Full triples — surface form annotated when it differs from this entity's canonical label.

scikit-learn → hasConcept → PCA ⓘ

PCA → basedOn → PCA self-linksurface differs ⓘ

subject surface form: PCA (scikit-learn)

this entity surface form: Principal Component Analysis

Cauchy interlacing theorem → usedIn → PCA ⓘ

this entity surface form: principal component analysis

random matrix theory → usedIn → PCA ⓘ

this entity surface form: principal component analysis