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Adaptive Graph Regularization Discriminant Nonnegative Matrix Factorization for Data Representation
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DOI：10.1109/ACCESS.2019.2933877.png)
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摘要
En 中文
Nonnegative matrix factorization, as a classical part-based representation method, has been widely used in pattern recognition, data mining and other fields. However, the traditional nonnegative matrix factorization directly factoring decomposes the original data, and the original data often contains a lot of redundancy and noise, which seriously affect the subsequent processing of the data. In this work, we propose an adaptive graph regularization discriminant nonnegative matrix factorization (AGDNMF) for image clustering. The AGDNMF algorithm makes full use of local structure information and a small amount of label information. In AGDNMF, the local structure information can be more accurate and the label information can prevent the points with the same label from being merged into one point. These two items are combined into the objective function of NMF. In addition, we provide the update rules for the corresponding optimization functions and prove its convergence. A large number of experiments on different data sets show that the proposed algorithm has good clustering performance.
Keyword:
Nonnegative matrix factorization
graph regularization
discriminative information
image clustering
data representation
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering用于图像聚类的图正则化非负低秩矩阵分解
PREAI
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分享收藏Local coordinate based graph-regularized NMF for image representation基于局部坐标的图正则化NMF图像表示.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
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