计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 473-477.
何孝文, 胡一飞, 王海平, 陈默
HE Xiao-wen, HU Yi-fei, WANG Hai-ping, CHEN Mo
摘要: 文中提出了一种新的在线形式的非负矩阵分解,即在线学习非负矩阵分解(OLNMF)。OLNMF算法采用了增量形式的非光滑模型,并采用“选择遗忘法”控制新样品和旧样品的权重,提高了算法的计算效率,减少了计算复杂度。OLNMF算法能处理大型的实时更新的数据集,并得到稀疏度更高的基矩阵。实验结果表明,在多个人脸数据集中,相对于INMF,ONMFO,Lp-INMF,OLNMF具有更好的稀疏性;在EEG数据集中,基于OLNMF的SVM分类方法能得到更好的分类准确率。
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