摘要
针对非负矩阵分解(NMF)具有一定的稀疏性,但不足以进行有效的分类的问题,为了获得特征提取过程中缺失的高维数据结构信息和隐藏信息,提高非负矩阵分解的低秩性与稀疏性,提出一种基于隐式低秩表示的非负矩阵分解模型(NLatMF)。该模型将隐式低秩算法提取的原始数据非负的低秩部分和隐式部分应用于非负矩阵分解,更有效地解决了分类问题。将该模型用于图像分类,通过在Yaleface等人脸数据库上仿真,结果表明:新模型有效提高了识别率。
Aiming at the problem that non-negative matrix factorization(NMF)has certain sparsity,but it is not enough for effective classification,in order to obtain high-dimensional data structure information and hidden information missing in the feature extraction process,improve the low rank and sparsity of the non-negative matrix factorization,a non-negative matrix factorization model based on latent low-rank representation(NLatMF)is proposed.Non-negative low-rank part and latent parts of the original data extracted by latent low-rank algorithm,applied to NMF,which solves classification problem more effectively.The improved model is used for image classification and the results show that the new model can improve the recognition rate effectively by simulating on the face database such as Yalefaces.
作者
杨国亮
龚曼
YANG Guoliang;GONG Man(School of Electrical Engineering and Automation,Jiangxi University of Science and Technology,Ganzhou 341000,China)
出处
《传感器与微系统》
CSCD
2020年第3期57-60,63,共5页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61763015,51365017,61305019)。
关键词
非负矩阵分解
特征提取
隐式低秩表示
稀疏性
图像分类
non-negative matrix factorization(NMF)
feature extraction
latent low-rank representation
sparseness
image classification
