Learning the parts of objects by non-negative matrix factorization
Learning the parts of objects by non-negative matrix factorization
复制标题
DOI：
10.1038/44565
复制
发表时间：
1999-10-21
期刊：
影响因子：
64.8
通讯作者：
Seung, HS
中科院分区：
文献类型：
--
作者：
Lee, DD;Seung, HS
对整体的感知是基于对其部分的感知吗？有心理学（1）和生理学（2，3）证据表明大脑中存在基于部分的表征，并且某些物体识别的计算理论依赖于这种表征（4，5）。但是对于大脑或计算机如何学习物体的各个部分却知之甚少。在此我们展示一种非负矩阵分解算法，它能够学习面部的各个部分以及文本的语义特征。这与其他方法形成对比，比如主成分分析和矢量量化，它们学习的是整体的而非基于部分的表征。非负矩阵分解因使用非负性约束而有别于其他方法。这些约束导致一种基于部分的表征，因为它们只允许相加而不允许相减的组合。当非负矩阵分解被实现为一个神经网络时，基于部分的表征凭借两个特性而出现：神经元的放电频率从不为负以及突触强度不改变符号。
Is perception of the whole based on perception of its parts? There is psychological(1) and physiological(2,3) evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations(4,5). But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.