Home / AIO Classes / MachineLearningTask / Principal Component Analysis

Principal Component Analysis

MachineLearningTask AIO:PrincipalComponentAnalysis NOT_REVIEWED

A dimensionality reduction method for analyzing large datasets with high-dimensional features per observation increasing data interpretability while preserving maximum information and enabling visualization.

Ontology IRI: https://w3id.org/aio/PrincipalComponentAnalysis

Ontology Structure & Relationships

Parent Classes

AIO:DimensionalityReduction (Dimensionality Reduction)

Branches

MachineLearningTask

Annotations & Sources

Synonyms:

PCA exact

Definition Sources:

https://en.wikipedia.org/wiki/Principal_component_analysis

Subsets:

https://w3id.org/aio/MachineLearningSubset

Curated AIMech Concepts Grounded to this Class

6 Concepts

Curated Concept	Kind	Mapping Relation	Rationale
Kernel Principal Component Analysis `AIMech:KernelPCA`	Algorithms	related_match	Kernel PCA uses ordinary PCA in an implicit centered feature space and recovers it for a linear kernel. The pinned task-hierarchy PCA record is related to this particular kernel algorithm; the mapping asserts neither exact identity nor a forced algorithm/task subclass relation.
Mixtures of Probabilistic Principal Component Analyzers `AIMech:MixturesOfProbabilisticPCA`	Model Families	related_match	The component covariance fits use local weighted principal subspaces; the mixture probability model is not identical to task-scoped PCA.
Oja Learning Rule `AIMech:OjaLearningRule`	Algorithms	related_match	For centered inputs and the stated conditions, this learning rule estimates one leading PCA direction. The pinned target is task-scoped PCA, not this synaptic update or a complete multi-component algorithm.
Probabilistic Principal Component Analysis `AIMech:ProbabilisticPCA`	Model Families	related_match	Its likelihood eigensolution spans a principal subspace, but the normalized latent model, posterior uncertainty and shrinkage differ from the task-scoped PCA record. This relation preserves the model/task distinction.
Spectral hashing `AIMech:SpectralHashing`	Algorithms	related_match	PCA supplies the coordinate axes used by the original spectral-hashing algorithm. This is a component relationship, not equivalence or a claim that the final nonlinear binary code is a PCA projection. The pinned PCA entry is task-scoped, and binary-code length is not necessarily lower than input dimension.
Supervised Principal Components `AIMech:SupervisedPrincipalComponents`	Algorithms	related_match	The procedure computes PCA after outcome-based screening, but adds supervised selection and prediction. The target is task-scoped PCA rather than this complete algorithm.

Import Provenance & Upstream Synchronization

Source Repo: AIO GitHub

Release Version: aio.json

Commit: 397851c547…

License: CC-BY-4.0

Imported At: 2026-10-10T07:34:10Z

SHA256: 8f741d71408c…

Source File: data/aio/PrincipalComponentAnalysis.yaml