扩展(谓词逻辑)
非负矩阵分解
矩阵分解
MATLAB语言
编码(集合论)
因式分解
基质(化学分析)
算法
数学
计算机科学
人工智能
域代数上的
纯数学
程序设计语言
特征向量
复合材料
集合(抽象数据类型)
材料科学
物理
量子力学
出处
期刊:Cornell University - arXiv
日期:2004-08-25
被引量:11
摘要
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.
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