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SupCP

swMATH ID: 24588
Software Authors: Lock, Eric F.; Li, Gen
Description: Supervised multiway factorization. We describe a probabilistic PARAFAC/CANDECOMP (CP) factorization for multiway (i.e., tensor) data that incorporates auxiliary covariates, {it SupCP}. SupCP generalizes the supervised singular value decomposition (SupSVD) for vector-valued observations, to allow for observations that have the form of a matrix or higher-order array. Such data are increasingly encountered in biomedical research and other fields. We use a novel likelihood-based latent variable representation of the CP factorization, in which the latent variables are informed by additional covariates. We give conditions for identifiability, and develop an EM algorithm for simultaneous estimation of all model parameters. SupCP can be used for dimension reduction, capturing latent structures that are more accurate and interpretable due to covariate supervision. Moreover, SupCP specifies a full probability distribution for a multiway data observation with given covariate values, which can be used for predictive modeling. We conduct comprehensive simulations to evaluate the SupCP algorithm. We apply it to a facial image database with facial descriptors (e.g., smiling / not smiling) as covariates, and to a study of amino acid fluorescence. Software is available at url{https://github.com/lockEF/SupCP}.
Homepage: https://github.com/lockEF/SupCP
Source Code: https://github.com/lockEF/SupCP
Dependencies: Matlab
Keywords: faces in the wild; dimension reduction; latent variables; parafac/candecomp; singular value decomposition; tensors
Related Software: MultiwayRegression; PubFig; LFW; CORALS; JIVE; tensorregress; MultiCluster; Algorithm 862; GitHub; Attribute2Image
Cited in: 3 Publications

Standard Articles

1 Publication describing the Software, including 1 Publication in zbMATH Year
Supervised multiway factorization. Zbl 1388.62176
Lock, Eric F.; Li, Gen
2018

Citations by Year