Wikipedia

Multilinear principal component analysis: Difference between revisions

Browse history interactively
← Previous editNext edit →
Content deleted Content added
VisualWikitext
m Frank Lauren Hitchcock introduced tensor rank decompositions in 1927 in physics and mathematics. Ledyard Tucker extended the idea in 1966 in psychometrics. This article did not mention Hitchcock's work, so I added a sentence mentioning it.
m Changed sentence structure in first paragraph to make it more aligned with encyclopedic tone
Line 1:
{{short description|Multilinear extension of principal component analysis}}
'''Multilinear principal component analysis''' ('''MPCA''') is a [[Multilinear algebra|multilinear]] extension of [[principal component analysis]] (PCA). MPCAthat is employedused into the analysis ofanalyze M-way arrays, i.e. a cube or hyper-cube of numbers, also informally referred to as a "data tensortensors". M-way arrays may be modeled by linear tensor models, such as CANDECOMP/Parafac, or by multilinear tensor models, such as multilinear principal component analysis (MPCA) or multilinear independent component analysis (MICA).
* linear tensor models such as CANDECOMP/Parafac, or
* multilinear tensor models, such as multilinear principal component analysis (MPCA), or multilinear independent component analysis (MICA), etc.
The origin of MPCA can be traced back to the [[Tensor rank decomposition|tensor rank decomposition]] introduced by [[Frank Lauren Hitchcock]] in 1927;<ref>{{Cite journal
| author = F. L. Hitchcock
Retrieved from "https://en.wikipedia.org/wiki/Multilinear_principal_component_analysis"