This paper investigates the problem of reconstructing hyperspectral (HS)...
This paper investigates the problem of recovering hyperspectral (HS) ima...
Crowd counting is critical for numerous video surveillance scenarios. On...
Symmetric nonnegative matrix factorization (SNMF) has demonstrated to be...
This paper proposes a multiobjective multitasking optimization evolution...
This paper explores the problem of clustering ensemble, which aims to co...
Deep subspace clustering network (DSC-Net) and its numerous variants hav...
In this paper, we first propose a graph neural network encoding method f...
This paper explores the problem of multi-view spectral clustering (MVSC)...
This paper intends to understand and to improve the working principle of...
Multi-task learning is a powerful method for solving multiple correlated...
Multi-objectivization is a term used to describe strategies developed fo...
Multi-objectivization is a term used to describe strategies developed fo...
This paper proposes a novel landscape smoothing method for the symmetric...
The surrogate-assisted optimization algorithm is a promising approach fo...
Conventional research attributes the improvements of generalization abil...
In this paper, an evolutionary many-objective optimization algorithm bas...
In this paper, we propose an efficient approximated rank one update for
...
Local search is a basic building block in memetic algorithms. Guided Loc...
This paper proposes a push and pull search (PPS) framework for solving
c...
In this report, we suggest nine test problems for multi-task multi-objec...
The decomposition-based method has been recognized as a major approach f...
Multi-objective evolutionary algorithms (MOEAs) have achieved great prog...
The balance between convergence and diversity is a key issue of evolutio...
Evolutionary algorithms (EAs) have been well acknowledged as a promising...