Oja rule
E899010
Oja rule is a normalized form of Hebbian learning used in neural networks to extract principal components by stabilizing synaptic weight growth.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Oja rule canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11002872 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oja rule Context triple: [Hebbian learning, hasVariant, Oja rule]
-
A.
Ojus
Ojus is an unincorporated community and census-designated place in northeastern Miami-Dade County, Florida, known for its residential neighborhoods and proximity to the city of Aventura.
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B.
Ojivolta
Ojivolta is a music production and songwriting duo known for their work with major hip-hop and pop artists, including frequent collaborations with Kanye West.
-
C.
Ojakkala
Ojakkala is a village in the municipality of Vihti in southern Finland, known for its rural residential character and proximity to the Helsinki metropolitan area.
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D.
OJAM
OJAM is the ICAO airport code for Amman Civil Airport, a domestic and regional airport serving Amman, Jordan.
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E.
Oa
Oa is the central planet and headquarters of the Green Lantern Corps in DC Comics, serving as the home of the Guardians of the Universe and the hub of intergalactic policing.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Oja rule Target entity description: Oja rule is a normalized form of Hebbian learning used in neural networks to extract principal components by stabilizing synaptic weight growth.
-
A.
Ojus
Ojus is an unincorporated community and census-designated place in northeastern Miami-Dade County, Florida, known for its residential neighborhoods and proximity to the city of Aventura.
-
B.
Ojivolta
Ojivolta is a music production and songwriting duo known for their work with major hip-hop and pop artists, including frequent collaborations with Kanye West.
-
C.
Ojakkala
Ojakkala is a village in the municipality of Vihti in southern Finland, known for its rural residential character and proximity to the Helsinki metropolitan area.
-
D.
OJAM
OJAM is the ICAO airport code for Amman Civil Airport, a domestic and regional airport serving Amman, Jordan.
-
E.
Oa
Oa is the central planet and headquarters of the Green Lantern Corps in DC Comics, serving as the home of the Guardians of the Universe and the hub of intergalactic policing.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
learning rule
ⓘ
synaptic plasticity rule ⓘ unsupervised learning algorithm ⓘ |
| appliesTo |
linear feedforward networks
ⓘ
single linear neuron ⓘ |
| assumes |
stationary input statistics
ⓘ
zero-mean input data ⓘ |
| basedOn | Hebbian learning ⓘ |
| category |
Hebbian learning rules
ⓘ
PCA learning rules ⓘ |
| contrastsWith | standard Hebbian rule without normalization ⓘ |
| convergesTo |
first principal component
ⓘ
leading eigenvector of input covariance matrix ⓘ |
| countryOfOrigin | Finland ⓘ |
| definedIn | “Simplified neuron model as a principal component analyzer” NERFINISHED ⓘ |
| ensures | bounded weight norm ⓘ |
| extendedTo | multi-neuron PCA networks ⓘ |
| field |
computational neuroscience
NERFINISHED
ⓘ
machine learning ⓘ neural computation ⓘ |
| hasComponent |
Hebbian term
ⓘ
weight decay term ⓘ |
| hasParameter | learning rate ⓘ |
| hasProperty | normalized Hebbian learning ⓘ |
| inspired | neural PCA algorithms ⓘ |
| introducedBy | Erkki Oja NERFINISHED ⓘ |
| learningType | unsupervised ⓘ |
| mathematicallyRelatedTo |
eigenvalue problem
ⓘ
stochastic gradient ascent ⓘ |
| maximizes | output variance under unit-norm constraint ⓘ |
| normalizes | weight vector magnitude ⓘ |
| optimizes | variance of neuron output ⓘ |
| prevents | unbounded synaptic weight growth ⓘ |
| publicationYear | 1982 ⓘ |
| publishedIn | Journal of Mathematical Biology NERFINISHED ⓘ |
| relatedTo |
Kohonen learning rule
ⓘ
Sanger rule NERFINISHED ⓘ |
| stabilizes | synaptic weights ⓘ |
| updateType | online learning rule ⓘ |
| usedFor |
adaptive signal processing
ⓘ
dimensionality reduction ⓘ feature extraction ⓘ principal component analysis ⓘ principal component extraction ⓘ subspace tracking ⓘ |
| usedIn |
neural networks
NERFINISHED
ⓘ
unsupervised neural learning ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Oja rule Description of subject: Oja rule is a normalized form of Hebbian learning used in neural networks to extract principal components by stabilizing synaptic weight growth.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.