Home / AIO Classes / MachineLearningTask / Dimensionality Reduction

Dimensionality Reduction

MachineLearningTask AIO:DimensionalityReduction NOT_REVIEWED

An unsupervised learning focused on the process of transforming data from a high-dimensional space into a lower-dimensional space while retaining meaningful properties of the original data.

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

Ontology Structure & Relationships

Parent Classes

AIO:UnsupervisedLearning (Unsupervised Learning)

Branches

MachineLearningTask

Annotations & Sources

Synonyms:

Dimension Reduction exact

Definition Sources:

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

Subsets:

https://w3id.org/aio/MachineLearningSubset

Curated AIMech Concepts Grounded to this Class

13 Concepts

Curated Concept	Kind	Mapping Relation	Rationale
Deep continuous clustering `AIMech:DeepContinuousClustering`	Algorithms	related_match	Learning a lower-dimensional nonlinear representation is an explicit jointly optimized part of DCC. The AIO target describes that task, not this particular algorithm or an assertion that its learned representation always preserves semantics.
Diffusion Maps `AIMech:DiffusionMaps`	Algorithms	related_match	A specific unsupervised embedding algorithm used for dimensionality reduction. The pinned target inherits the machine-learning-task hierarchy; this association does not assert algorithm/task identity or subclass membership. RepresentationLearning in this pin specifically denotes a deep neural network, and EmbeddingLayer denotes an integer-index lookup layer, neither of which defines this method.
Isomap `AIMech:Isomap`	Algorithms	related_match	A specific unsupervised embedding algorithm used for dimensionality reduction. The pinned target inherits the machine-learning-task hierarchy; this association does not assert algorithm/task identity or subclass membership. RepresentationLearning in this pin specifically denotes a deep neural network, and EmbeddingLayer denotes an integer-index lookup layer, neither of which defines this method.
Laplacian Eigenmaps `AIMech:LaplacianEigenmaps`	Algorithms	related_match	A specific unsupervised embedding algorithm used for dimensionality reduction. The pinned target inherits the machine-learning-task hierarchy; this association does not assert algorithm/task identity or subclass membership. RepresentationLearning in this pin specifically denotes a deep neural network, and EmbeddingLayer denotes an integer-index lookup layer, neither of which defines this method.
LargeVis `AIMech:LargeVis`	Algorithms	related_match	A specific algorithm for constructing lower-dimensional representations, associated with the pinned dimensionality-reduction task. The target inherits UnsupervisedLearning under MachineLearningTask; related_match avoids treating the algorithm as identical to or a subclass of that task. Shared embedding components do not make this concept identical to PCA, MDS or t-SNE.
Locally Linear Embedding `AIMech:LocallyLinearEmbedding`	Algorithms	related_match	A specific unsupervised embedding algorithm used for dimensionality reduction. The pinned target inherits the machine-learning-task hierarchy; this association does not assert algorithm/task identity or subclass membership. RepresentationLearning in this pin specifically denotes a deep neural network, and EmbeddingLayer denotes an integer-index lookup layer, neither of which defines this method.
Neighbourhood components analysis `AIMech:NeighborhoodComponentsAnalysis`	Algorithms	related_match	The rectangular NCA configuration learns a lower-dimensional projection using labels. The AIO target is placed under UnsupervisedLearning; this related association preserves the shared reduction operation without asserting that supervised NCA is an unsupervised subclass.
Nonnegative Matrix Factorization `AIMech:NonnegativeMatrixFactorization`	Mathematical Methods	related_match	A low-rank NMF representation performs dimensionality reduction, a task-scoped AIO concept. The related mapping preserves the mathematical-method/task distinction and does not require every permitted factor size to reduce dimension.
PHATE `AIMech:PHATE`	Algorithms	related_match	A specific algorithm for constructing lower-dimensional representations, associated with the pinned dimensionality-reduction task. The target inherits UnsupervisedLearning under MachineLearningTask; related_match avoids treating the algorithm as identical to or a subclass of that task. Shared embedding components do not make this concept identical to PCA, MDS or t-SNE.
PaCMAP `AIMech:PaCMAP`	Algorithms	related_match	A specific algorithm for constructing lower-dimensional representations, associated with the pinned dimensionality-reduction task. The target inherits UnsupervisedLearning under MachineLearningTask; related_match avoids treating the algorithm as identical to or a subclass of that task. Shared embedding components do not make this concept identical to PCA, MDS or t-SNE.
Parametric UMAP `AIMech:ParametricUMAP`	Algorithms	related_match	A specific algorithm for constructing lower-dimensional representations, associated with the pinned dimensionality-reduction task. The target inherits UnsupervisedLearning under MachineLearningTask; related_match avoids treating the algorithm as identical to or a subclass of that task. Shared embedding components do not make this concept identical to PCA, MDS or t-SNE.
TriMap `AIMech:TriMap`	Algorithms	related_match	A specific algorithm for constructing lower-dimensional representations, associated with the pinned dimensionality-reduction task. The target inherits UnsupervisedLearning under MachineLearningTask; related_match avoids treating the algorithm as identical to or a subclass of that task. Shared embedding components do not make this concept identical to PCA, MDS or t-SNE.
UMAP `AIMech:UMAP`	Algorithms	related_match	A specific unsupervised embedding algorithm used for dimensionality reduction. The pinned target inherits the machine-learning-task hierarchy; this association does not assert algorithm/task identity or subclass membership. RepresentationLearning in this pin specifically denotes a deep neural network, and EmbeddingLayer denotes an integer-index lookup layer, neither of which defines this method.

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/DimensionalityReduction.yaml