本文构造的稀疏非负矩阵分解模型很有借鉴意义。
尤其是对于稀疏的定义，如何改造一个已有的向量，使之符合设定的稀疏性。
同其他稀疏模型相比，本文不是在objective function中加入稀疏约束项，而是通过直接改造学出的向量。
本文代码model代码ref： https://github.com/vgeek-z/MATLAB
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Non-negative Matrix Factorization with Sparseness Constraints | |||||||||||
| 文献作者 | Patrik O. Hoyer | ||||||||||
| 文献发表年限 | 2004 | ||||||||||
| 文献关键字 | |||||||||||
| non-negative matrix factorization, sparseness, data-adaptive representations；稀疏矩阵分解； | |||||||||||
| 摘要描述 | |||||||||||
| Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we show how explicitly incorporating the notion of ‘sparseness’ improves the found decompositions. Additionally, we provide complete MATLAB code both for standard NMF and for our extension. Our hope is that this will further the application of these methods to solving novel data-analysis problems. | |||||||||||
