.png){kind=icon}
Return
Biobjective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models
.png){kind=icon}
OA
AI
DOI：10.1109/TGRS.2016.2535298.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Nonnegative matrix factorization (NMF) is a powerful class of feature extraction techniques that has been successfully applied in many fields, particularly in signal and image processing. Current NMF techniques have been limited to a single-objective optimization problem, in either its linear or nonlinear kernel-based formulation. In this paper, we propose to revisit the NMF as a multiobjective problem, particularly a biobjective one, where the objective functions defined in both input and feature spaces are taken into account. By taking the advantage of the sum-weighted method from the literature of multiobjective optimization, the proposed biobjective NMF determines a set of nondominated, Pareto optimal, solutions. Moreover, the corresponding Pareto front is approximated and studied. Experimental results on unmixing synthetic and real hyperspectral images confirm the efficiency of the proposed biobjective NMF compared with the state-of-the-art methods.
Keywords:
Hyperspectral image
kernel machines
nonnegative matrix factorization (NMF)
Pareto optimal
unmixing problem
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
Organization
Cited Papers
ShareSaveMultiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach
PREAI
ShareSaveSubsystem identification through dimensionality reduction of large-scale gene expression data.png)
GENOME RESEARCHOAAI
GENOME RESEARCHIF5.5
OAAIShareSaveShareSavePrintable cellulose-based electroconductive composites for sensing elements in paper electronics
OAAI
OAAIShareSave
View more