Statements (30)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
tensor decomposition method
|
| gptkbp:alsoKnownAs |
gptkb:CANDECOMP/PARAFAC_decomposition
|
| gptkbp:computes |
gradient descent
alternating least squares |
| gptkbp:decomposes |
gptkb:Tensor
|
| gptkbp:decomposesInto |
sum of rank-one tensors
|
| gptkbp:foundIn |
latent factors
|
| gptkbp:generalizes |
matrix singular value decomposition
|
| gptkbp:hasApplication |
recommender systems
topic modeling neuroscience data analysis |
| https://www.w3.org/2000/01/rdf-schema#label |
CP decomposition
|
| gptkbp:introduced |
gptkb:Carroll
Harshman |
| gptkbp:introducedIn |
1970
|
| gptkbp:limitation |
computational complexity
uniqueness issues |
| gptkbp:relatedTo |
gptkb:Tucker_decomposition
tensor rank nonnegative tensor factorization |
| gptkbp:requires |
number of components (rank)
|
| gptkbp:usedIn |
gptkb:machine_learning
gptkb:signal_processing gptkb:chemometrics psychometrics multilinear algebra |
| gptkbp:bfsParent |
gptkb:CANDECOMP/PARAFAC_decomposition
gptkb:Higher-Order_SVD_(HOSVD) gptkb:Tucker_factorization |
| gptkbp:bfsLayer |
7
|