Latent structure
非负矩阵分解 (NMF)
非负矩阵分解 (NMF) 是一类算法，由 Lee 和 Seung 在其 1999 年的里程碑式 Nature 论文中提出，它将一个非负数据矩阵 V 分解为两个低秩非负矩阵 W（基分量）和 H（编码系数）的乘积。与 PCA 或 SVD 不同，非负性约束迫使算法学习严格的加性、基于部分的表示，使得分解出的因子可以直接解释为原始数据的组成部分。
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来源
- Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791. DOI: 10.1038/44565 ↗
- Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562. link ↗
- Cichocki, A., Zdunek, R., Phan, A. H., & Amari, S. (2009). Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. Wiley. ISBN: 978-0-470-74666-0
如何引用本页
ScholarGate. (2026, June 3). Non-negative Matrix Factorization (Lee & Seung, 1999). ScholarGate. https://scholargate.app/zh/machine-learning/non-negative-matrix-factorization
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