Abstract. A novel sparsity optimization method is proposed to select features for multi-class classification problems by directly optimizing a ℓ2,p -norm.

A novel sparsity optimization method is proposed to select features for multi-class classification problems by directly optimizing a l 2,p -norm (0 < p ≤ 1) ...

An efficient iterative algorithm with proved convergence is proposed to solve the direct sparsity optimization problem that is nonsmooth and non-convex when ...

A novel sparsity optimization method is proposed to select features for multi-class classification problems by directly optimizing a ℓ2,p-norm (0 < p ≤ 1) based ...

Abstract: Remote sensing image classification plays an important role in a wide range of applications and has caused widely concerns.

Title. Sparsity-regularized feature selection for multi-class remote sensing image classification. Authors. Chen, Tao; Zhao, Ye; Guo, Yanrong. Abstract ...

A remote sensing image classification algorithm based on the sparse regularized feature learning method using direct sparsity optimization-based feature ...

Jan 25, 2019 · To this end, this paper proposes a remote sensing image classification algorithm based on the sparse regularized feature learning method.

Mar 30, 2024 · ISEMCC transforms the problem of interval feature selection and decision-level fusion into a nonnegative sparse optimization problem. The sparse ...

Direct Sparsity Optimization Based Feature. Selection for Multi-Class Classification. the 25th International. Joint Conference on Artificial Intelligence ...