基于随机方差调整梯度的非负矩阵分解

针对求解非负矩阵分解的乘性更新规则存在计算复杂度高且迭代效率低等缺点,提出一种随机方差参数调整梯度的方法.将方差缩减策略和乘性更新规则相结合,通过引入一个调整随机梯度估计量的参数校正梯度下降方向使其偏差与方差达到平衡,从而能快速、准确地逼近最优解.在真实数据集上进行仿真实验,结果验证了该算法的可行性和有效性.

Non-negative Matrix Factorization Based on Stochastic Variance Adjusted Gradient

For the multiplicative updating rule in solving the non-negative matrix factorization, there were some shortcomings of high computational complexity and low iterative efficiency. We proposed a stochastic variance parameter adjusted gradient method. Combining the variance reduction strategy with the multiplicative updating rule, a parameter was adopted to adjust the stochastic gradient estimator, and the gradient descent direction was corrected to balance its deviation and variance, so as to reach the optimal solution quickly and accurately. Experiments were carried out on the real data sets, and the results verify the feasibility and effectiveness of the proposed algorithm.