一种自适应非负矩阵分解算法

首先, 通过引入自适应策略, 提出一种基于梯度下降自适应策略的非负矩阵分解算法. 其次, 通过比较重构非负矩阵的距离度量并自适应调节分解, 解决了传统非负矩阵分解方法在求解过程引入的随机性和基向量数目问题, 且该算法生成的基向量更具代表性. 最后, 以对吉林大学某学院本科生成绩进行分析和验证为例考察算法的有效性. 实验结果表明, 自适应非负矩阵分解方法重构矩阵较传统非负矩阵方法的鲁棒性更好, 并将错误率降低20.16%.

A Self adaptive Nonnegative Matrix Factorization Algorithm

Firstly, by introducing adaptive strategy, we proposed a self adaptive nonnegative matrix factorization based on gradient descent. Secondly, by comparing the distance between reconstructed nonnegative matrix and self adaptive regulation, the problems of  randomness and the number of basic vectors validation for traditional nonnegative matrix factorization were solved, and the basic vectors generated by the algorithm were more representative. Finally, taking the analysis and validation of undergraduate achievement of a college of Jilin University as an example, we investigated effectiveness of the proposed algorithm. The experimental results show that  compared with the traditional nonnegative matrix method, the self adaptive nonnegetive matrix factorization method has better robutness and reduces the error rate by 20.16%.