| 摘要: |
| 针对基因表达数据高维、高噪声等特点，提出了一种基于正交约束的负矩阵分解算法；该算法将正交约束引入到β散度矩阵分解的准则函数中进行优化求解，用梯度下降方法得出矩阵分解的乘积迭代规则，并利用分解项来降低特征空间的维度，将得到的向量用于K均值聚类；实验中选择5种肿瘤基因表达数据，实验结果表明：改进的算法分解所得矩阵在聚类效果上明显优于其他的方法. |
| 关键词: 散度 正交约束 梯度下降 聚类 |
| DOI： |
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| Application of Improved β-NMF to Gene Clustering |
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YOU Chun zhi，CUI Jian
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| Abstract: |
| In view of gene expression characteristics of high dimension and high noise，orthogonal subspace matrix decomposition algorithm is proposed based on beta divergence matrix decomposition by introducing the orthogonal constraint to objective function for optimization and solution. The iteration rules of the matrix decomposition is given by the gradient descent method，the decomposition items are used to reduce the dimension of feature space，and the derived vector is used for k means clustering. Five tumor gene expression data are chosen for the experiment，and the results show that the improved algorithm matrix decomposition clustering is obviously better than other methods. |
| Key words: non negative matrix factorization beta divergence orthogonal constraint gradient descent clustering |
