本文提出了一种基于非负矩阵稀疏分解(Non-negative Matrix Factorization with Sparseness Constraints, NMFs)和RBF神经网络的人脸识别方法。
基于6个网页-相关网页
本文主要是论述稀疏非负矩阵分解算法在矿产资源定量预测中的应用研究。
In this article, the sparse non-negative matrix factorization algorithm is applied to quantitative predict the mineral resources.
非负矩阵分解算法简单，易于实现，并且具有降维、收敛和稀疏等特性。
Moreover, NMF algorithm is simple and easy to implement and it has features such as dimension-lowering and sparse convergence.
应用推荐