Dimension-wise sparse low-rank approximation of a matrix with application to variable selection in high-dimensional integrative analyzes of association.

Poythress, J C; Park, Cheolwoo; Ahn, Jeongyoun

Poythress, J C; Park, Cheolwoo; Ahn, Jeongyoun.

Afiliación

Poythress JC; Department of Mathematics and Statistics, University of New Hampshire, Durham, NH, USA.
Park C; Department of Mathematical Sciences, KAIST, Daejeon, The Republic of Korea.
Ahn J; Department of Industrial and Systems Engineering, KAIST, Daejeon, The Republic of Korea.

J Appl Stat ; 49(15): 3889-3907, 2022.

Article en En | MEDLINE | ID: mdl-36324486

Palabras clave

High dimension low sample size; multimodal data; nuclear norm; sparse canonical correlation analysis

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Risk_factors_studies Idioma: En Revista: J Appl Stat Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

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