1 code implementation • 23 Jun 2016 • Devis Tuia, Remi Flamary, Michel Barlaud
In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing.
1 code implementation • 22 Aug 2022 • Cyprien Gille, Frederic Guyard, Michel Barlaud
Experiments show that the SSAE outperforms Label Propagation and Spreading and the Fully Connected Neural Network both on a synthetic dataset and on two real-world biological datasets.
no code implementations • 8 Nov 2017 • Cyprien Gilet, Marie Deprez, Jean-Baptiste Caillau, Michel Barlaud
The projection-gradient step is a method of splitting type, with exact projection on the $\ell^1$ ball to promote sparsity.
no code implementations • 6 Jun 2015 • Michel Barlaud, Wafa Belhajali, Patrick L. Combettes, Lionel Fillatre
This paper deals with sparse feature selection and grouping for classification and regression.
no code implementations • 5 Feb 2019 • Michel Barlaud, Antonin Chambolle, Jean-Baptiste Caillau
This paper deals with supervised classification and feature selection in high dimensional space.
no code implementations • 7 Sep 2020 • Guillaume Perez, Sebastian Ament, Carla Gomes, Michel Barlaud
In this paper we propose three new efficient algorithms for projecting any vector of finite length onto the weighted $\ell_1$ ball.
no code implementations • 9 Sep 2022 • Cyprien Gille, Frédéric Guyard, Marc Antonini, Michel Barlaud
Recently, convolutional auto-encoders (CAE) were introduced for image coding.
1 code implementation • 19 Jul 2023 • Guillaume Perez, Laurent Condat, Michel Barlaud
In this paper, we introduce a new projection algorithm for the $\ell_{1,\infty}$ norm ball.