principal component analysis
GPTKB entity
Statements (52)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
dimensionality reduction method
statistical technique |
| gptkbp:abbreviation |
gptkb:PCA
|
| gptkbp:assumes |
large variances have important structure
linearity |
| gptkbp:field |
gptkb:artificial_intelligence
gptkb:data_visualization gptkb:chemometrics data science ecology exploratory data analysis finance genomics neuroscience psychometrics statistics bioinformatics image compression pattern recognition face recognition |
| gptkbp:goal |
identify principal components
maximize variance reduce dimensionality of data |
| https://www.w3.org/2000/01/rdf-schema#label |
principal component analysis
|
| gptkbp:input |
data matrix
|
| gptkbp:introducedIn |
1901
|
| gptkbp:inventedBy |
gptkb:Karl_Pearson
|
| gptkbp:limitation |
assumes orthogonality of components
not suitable for non-linear data sensitive to scaling |
| gptkbp:output |
explained variance
principal components |
| gptkbp:relatedTo |
gptkb:factor_analysis
eigenvalue decomposition singular value decomposition |
| gptkbp:step |
center data
compute covariance matrix compute eigenvectors and eigenvalues project data onto principal components sort eigenvectors by eigenvalues |
| gptkbp:supportsAlgorithm |
gptkb:kernel_PCA
gptkb:t-SNE independent component analysis linear discriminant analysis multidimensional scaling |
| gptkbp:usedIn |
gptkb:machine_learning
gptkb:signal_processing data analysis image processing |
| gptkbp:bfsParent |
gptkb:Karl_Pearson
gptkb:machine_learning |
| gptkbp:bfsLayer |
4
|