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Applications of Spectral Gradient Algorithm for Solving Matrix ℓ(2,1)-Norm Minimization Problems in Machine Learning
The main purpose of this study is to propose, then analyze, and later test a spectral gradient algorithm for solving a convex minimization problem. The considered problem covers the matrix ℓ(2,1)-norm regularized least squares which is widely used in multi-task learning for capturing the joint featu...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Public Library of Science
2016
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5115710/ https://www.ncbi.nlm.nih.gov/pubmed/27861526 http://dx.doi.org/10.1371/journal.pone.0166169 |