Search Results for author:
Found 9 papers, 4 papers with code
Clustering with feature selection using alternating minimization, Application to computational biology
The projection-gradient step is a method of splitting type, with exact projection on the $\ell^1$ ball to promote sparsity.
Classification and regression using an outer approximation projection-gradient method
This paper deals with sparse feature selection and grouping for classification and regression.
Robust supervised classification and feature selection using a primal-dual method
This paper deals with supervised classification and feature selection in high dimensional space.
Efficient Projection Algorithms onto the Weighted l1 Ball
In this paper we propose three new efficient algorithms for projecting any vector of finite length onto the weighted $\ell_1$ ball.
Learning sparse auto-encoders for green AI image coding
Recently, convolutional auto-encoders (CAE) were introduced for image coding.
Near-Linear Time Projection onto the $\ell_{1,\infty}$ Ball; Application to Sparse Autoencoders
In this paper, we introduce a new projection algorithm for the $\ell_{1,\infty}$ norm ball.
Multi-level projection with exponential parallel speedup; Application to sparse auto-encoders neural networks
In this paper, we propose a new bi-level projection method for which we show that the time complexity for the $\ell_{1,\infty}$ norm is only $\mathcal{O}\big(n m \big)$ for a matrix in $\mathbb{R}^{n\times m}$, and $\mathcal{O}\big(n + m \big)$ with full parallel power.
Semi-supervised classification using a supervised autoencoder for biomedical applications
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.
Non-convex regularization in remote sensing
In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing.
