Home / AIO Classes / MachineLearningTask / Least-squares Analysis

Least-squares Analysis

MachineLearningTask AIO:LeastsquaresAnalysis NOT_REVIEWED

A regression analysis which approximates the solution of overdetermined systems by minimizing the sum of the squares of the residuals.

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

Ontology Structure & Relationships

Parent Classes

AIO:RegressionAnalysis (Regression Analysis)

Branches

MachineLearningTask

Annotations & Sources

Definition Sources:

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

Subsets:

https://w3id.org/aio/MachineLearningSubset

Curated AIMech Concepts Grounded to this Class

5 Concepts

Curated Concept	Kind	Mapping Relation	Rationale
Correlation-based Hammerstein identification `AIMech:CorrelationBasedHammersteinIdentification`	Algorithms	related_match	Both stages use residual-squared parameter fitting after the source’s correlation-based separation. The pinned target describes a general fitting analysis, whereas this record specifies an ordered identification procedure with excitation and block-structure assumptions. A related mapping reflects its component mechanism without claiming exact identity or an asserted subclass.
Correlation-based Wiener identification `AIMech:CorrelationBasedWienerIdentification`	Algorithms	related_match	Both stages use residual-squared parameter fitting after the source’s correlation-based separation. The pinned target describes a general fitting analysis, whereas this record specifies an ordered identification procedure with excitation and block-structure assumptions. A related mapping reflects its component mechanism without claiming exact identity or an asserted subclass.
Orthogonal least squares forward selection `AIMech:OrthogonalLeastSquaresForwardSelection`	Algorithms	related_match	The algorithm uses least-squares projection at every candidate augmentation, directly relating it to the target’s residual-squared fitting task. The pinned target describes overdetermined regression analysis, whereas this record denotes a specific greedy subset-selection algorithm and also allows overcomplete candidate sets. Related mapping preserves that algorithm/task and scope distinction; it is not exact identity or an asserted subclass.
Pseudo-linear regression estimation `AIMech:PseudoLinearRegressionEstimation`	Algorithms	related_match	The squared-residual criterion and recursive least-squares algebra directly relate these parameter updates to residual-squared fitting. The pinned target describes an overdetermined fitting analysis, not this recursive estimator; the pseudo-linear approximation also need not minimize the full nonlinear prediction-error objective. A related mapping preserves these differences.
Recursive prediction-error estimation `AIMech:RecursivePredictionErrorEstimation`	Algorithms	related_match	The squared-residual criterion and recursive least-squares algebra directly relate these parameter updates to residual-squared fitting. The pinned target describes an overdetermined fitting analysis, not this recursive estimator; the pseudo-linear approximation also need not minimize the full nonlinear prediction-error objective. A related mapping preserves these differences.

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/LeastsquaresAnalysis.yaml