动态学习的非负矩阵分解算法

对现有增量型非负矩阵分解算法存在的一些缺陷进行改进，给出了一个基于误差判断的增量算法有效性准则.在此基础上，利用增加样本前的非负矩阵分解结果进行增量分解初始化，提出了一种新的动态非负矩阵分解算法.在多个数据集上的实验结果表明该算法可以实现对基矩阵和编码矩阵的即时更新，且具有较低的计算复杂度，在处理动态数据集时，还可有效识别噪声点，是一个有效的动态分解算法.

A dynamic learning algorithm based on non-negative matrix factorization

To improve the performance of the incremental non negative matrix factorization algorithm, error estimation criteria for judging the effectiveness of the incremental algorithm was presented. Then, a new dynamic non negative matrix factorization algorithm was proposed whereby incremental factorization was initialized with the already factorized matrices before adding new samples. Experimental results on a number of data sets showed that the proposed algorithm is capable of instantly updating both the base matrix and the code matrix. Another benefit of the method is that the computational complexity is relatively low. The proposed algorithm can also identify noise points when dealing with dynamic data. So it is a feasible and effective dynamic factorization algorithm.