• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >A Collective Neurodynamic Optimization Approach to Nonnegative Matrix Factorization
【24h】

A Collective Neurodynamic Optimization Approach to Nonnegative Matrix Factorization

机译：非负矩阵分解的集体神经动力学优化方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Nonnegative matrix factorization (NMF) is an advanced method for nonnegative feature extraction, with widespread applications. However, the NMF solution often entails to solve a global optimization problem with a nonconvex objective function and nonnegativity constraints. This paper presents a collective neurodynamic optimization (CNO) approach to this challenging problem. The proposed collective neurodynamic system consists of a population of recurrent neural networks (RNNs) at the lower level and a particle swarm optimization (PSO) algorithm with wavelet mutation at the upper level. The RNNs act as search agents carrying out precise local searches according to their neurodynamics and initial conditions. The PSO algorithm coordinates and guides the RNNs with updated initial states toward global optimal solution(s). A wavelet mutation operator is added to enhance PSO exploration diversity. Through iterative interaction and improvement of the locally best solutions of RNNs and global best positions of the whole population, the population-based neurodynamic systems are almost sure able to achieve the global optimality for the NMF problem. It is proved that the convergence of the group-best state to the global optimal solution with probability one. The experimental results substantiate the efficacy and superiority of the CNO approach to bound-constrained global optimization with several benchmark nonconvex functions and NMF-based clustering with benchmark data sets in comparison with the state-of-the-art algorithms.
机译：非负矩阵分解（NMF）是一种用于非负特征提取的高级方法，具有广泛的应用。但是，NMF解决方案通常需要解决具有非凸目标函数和非负约束的全局优化问题。本文提出了一种集体神经动力学优化（CNO）方法来解决这一具有挑战性的问题。提出的集体神经动力学系统由较低级别的一组递归神经网络（RNN）和较高级别的具有小波突变的粒子群优化（PSO）算法组成。 RNN充当搜索代理，根据其神经动力学和初始条件进行精确的本地搜索。 PSO算法协调并引导具有更新的初始状态的RNN朝向全局最优解。添加了小波突变算子以增强PSO探索多样性。通过迭代交互和改进RNN的局部最优解以及整个总体的全局最佳位置，基于群体的神经动力学系统几乎可以肯定地实现NMF问题的全局最优性。证明了群最优状态收敛于全局最优解的概率为1。与最新算法相比，实验结果证实了CNO方法具有多个基准非凸函数和基于NMF聚类与基准数据集的约束受限全局优化的有效性和优越性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号