Efficient Projection onto the $\ell_{\infty,1}$ Mixed-Norm Ball Using a Newton Root Search Method
Mixed norms that promote structured sparsity have numerous applications in signal processing and machine learning problems. In this work, we present a new algorithm, based on a Newton root search technique, for computing the projection onto the ℓ∞,1 ball, which has found application in cognitive neu...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | Perú |
| Institución: | Consejo Nacional de Ciencia Tecnología e Innovación |
| Repositorio: | CONCYTEC-Institucional |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.concytec.gob.pe:20.500.12390/1281 |
| Acceso en línea: | https://hdl.handle.net/20.500.12390/1281 https://doi.org/10.1137/18m1212525 |
| Access Level: | acceso abierto |
| Palabra clave: | regularización de la proyección Normas mixtas espaciosidad estructurada encontrar la raíz https://purl.org/pe-repo/ocde/ford#1.01.02 |
| Sumario: | Mixed norms that promote structured sparsity have numerous applications in signal processing and machine learning problems. In this work, we present a new algorithm, based on a Newton root search technique, for computing the projection onto the ℓ∞,1 ball, which has found application in cognitive neuroscience and classification tasks. Numerical simulations show that our proposed method is between 8 and 10 times faster on average, and up to 20 times faster for very sparse solutions, than the previous state of the art. Tests on real functional magnetic resonance image data show that, for some data distributions, our algorithm can obtain speed improvements by a factor of between 10 and 100, depending on the implementation |
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