Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

We propose a filtering feature selection framework that considers subsets of features as
paths in a graph, where a node is a feature and an edge indicates pairwise (customizable) …

Statistical methods for genome-wide association studies (GWAS) continue to improve.
However, the increasing volume and variety of genetic and genomic data make …

We propose a constructive approach to estimating sparse, high-dimensional linear
regression models. The approach is a computational algorithm motivated from the KKT …

We survey optimization problems that involve the cardinality of variable vectors in
constraints or the objective function. We provide a unified viewpoint on the general problem …

Algorithms based on the hard thresholding principle have been well studied with sounding
theoretical guarantees in the compressed sensing and more general sparsity-constrained …

This paper is concerned with the hard thresholding operator which sets all but the k largest
absolute elements of a vector to zero. We establish a tight bound to quantitatively …

In this paper, we investigate L 0-regularized logistic regression models, and design two fast
and efficient algorithms for high-dimensional correlated data and massive data, respectively …

Meta-learning is a powerful paradigm for few-shot learning. Although with remarkable
success witnessed in many applications, the existing optimization based meta-learning …

This work addresses the problem of cross-domain few-shot classification which aims at
recognizing novel categories in unseen domains with only a few labeled data samples. We …