Unsupervised Learning
MachineLearningTask AIO:UnsupervisedLearning NOT_REVIEWED
A machine learning task focused on algorithms that learn patterns from unlabeled data.
Ontology IRI:
https://w3id.org/aio/UnsupervisedLearning
Ontology Structure & Relationships
Parent Classes
Branches
Annotations & Sources
Definition Sources:
Subsets:
https://w3id.org/aio/MachineLearningSubset
Curated AIMech Concepts Grounded to this Class
10 Concepts| Curated Concept | Kind | Mapping Relation | Rationale |
|---|---|---|---|
Competitive learning
AIMech:CompetitiveLearning |
Learning Paradigms | related_match | The inspected competitive representations are learned from unlabeled input patterns, which supports association with the pinned unsupervised-learning task. The local subject specifies competition-based adaptation, including distinct hard and soft realizations, rather than every unsupervised task or one universal algorithm. This is a qualified related mapping, not equivalence or an asserted upstream subclass relation. |
FastICA
AIMech:FastICA |
Algorithms | related_match | FastICA estimates source directions from observations without labeled source targets. The task-scoped AIO class is related to this fitting algorithm, without asserting algorithm/task identity or a network architecture. |
Fixed-Component Mixture-Weight EM
AIMech:FixedComponentMixtureWeightEM |
Algorithms | related_match | The procedure estimates latent component proportions from an observed distribution without component labels. This relates the fitting algorithm to the AIO unlabeled-learning task without asserting equivalence or subclass membership. |
Infomax Independent Component Analysis
AIMech:InfomaxICA |
Algorithms | related_match | The algorithm estimates source-separating parameters from observations without labeled source targets. The task-scoped AIO target is related to this algorithm without equating their abstraction levels. |
Lee-Seung Multiplicative Updates
AIMech:LeeSeungMultiplicativeUpdates |
Algorithms | related_match | These algorithms fit additive latent factors from a supplied data matrix without target labels. The AIO target is a learning task, so the relationship does not assert algorithm/task equivalence or subclass membership. |
Local outlier factor
AIMech:LocalOutlierFactor |
Algorithms | related_match | LOF infers neighborhood-relative outlier scores from unlabeled observations, giving a related algorithm-to-task association with AIO:UnsupervisedLearning. The target is a MachineLearningTask; this link is neither exact equivalence nor a subclass assertion. The core score does not itself assign two classes, so BinaryClassification and its supervised ancestry are not selected. Clustering and OPTICS/DBSCAN organize clusters or reachability orderings, whereas LOF does not require a partition. All three AIO KnearestNeighbor classes specify label voting or response averaging, not local reachability ratios; RegressionAnalysis requires dependent/independent-variable relationships. DimensionalityReduction, DataPreparation and SelectionAndSamplingBias do not identify this scoring procedure. Native neighbor-ratio and distance-norm domain descriptions, PDD, supervised metric learning and density clustering have different outputs or neighborhood rules. |
OPTICS outlier factor
AIMech:OPTICSOutlierFactor |
Algorithms | related_match | OPTICS-OF computes relative local outlier scores from unlabeled observations, supporting a related algorithm-to-task association with AIO:UnsupervisedLearning. The target is a MachineLearningTask, so the link asserts neither equivalence nor subclassing. BinaryClassification and its supervised ancestry do not describe the unthresholded core. Clustering, DBSCAN and OPTICS produce cluster structure or a reachability ordering; this standalone score needs neither a partition nor an OPTICS traversal. The three AIO KnearestNeighbor classes concern voting or response averaging, unlike these density ratios. RegressionAnalysis, DimensionalityReduction, DataPreparation and SelectionAndSamplingBias do not identify the procedure. Native LOF differs by explicit self exclusion; domain-description and metric-learning methods have different rules or outputs. |
Oja Learning Rule
AIMech:OjaLearningRule |
Algorithms | related_match | The update uses observations without supervised targets. This is an algorithm related to the pinned learning task, not a forced subclass of its preprocessing-defined TrainingStrategy parent. |
One-class support vector machine
AIMech:OneClassSupportVectorMachine |
Algorithms | related_match | The AIO target describes the task of learning patterns from unlabeled observations, whereas this local record specifies one support-estimation algorithm. The relation captures that learning setting without equating an algorithm with a task or asserting a subclass edge. Users may supply a selected target class; the mathematical objective does not require negative training examples. BinaryClassification and Classification inherit supervised task scope and do not identify this objective. KernelLayer is a neural component, and generic Model/Network nodes do not identify the estimator. |
Topographic Independent Component Analysis
AIMech:TopographicICA |
Model Families | related_match | The model is estimated from unlabeled observations to expose latent component structure. The target is a task, so this mapping does not assert model/task equivalence. |
Import Provenance & Upstream Synchronization
Source Repo: AIO GitHub
Release Version:
aio.jsonCommit:
397851c547…License: CC-BY-4.0
Imported At: 2026-10-10T07:34:10Z
SHA256:
8f741d71408c…
Source File:
data/aio/UnsupervisedLearning.yaml