通过非负矩阵因数分解学习对象的部分.
Nature
|November 5, 1999
概括
这项研究引入了一种新的算法,即非负矩阵分解 (NMF),它学习对象部分以更好地识别. 与其他方法不同,NMF使用约束来实现加法组合,揭示基于部分的表示.
科学领域:
- 计算神经科学是一种计算神经科学.
- 机器学习 机器学习
- 认知心理学 认知心理学
背景情况:
- 心理和生理学证据支持大脑中基于部分的表征.
- 对象识别的计算理论通常使用基于部分的表示.
- 大脑或计算机学习物体部分的机制仍然是一个悬而未决的问题.
研究的目的:
- 展示一种能够学习对象部分的算法.
- 将这种方法与学习整体表示的方法进行对比.
- 调查非消极性约束在表示学习中的作用.
主要方法:
- 开发了一个非负矩阵因子化 (NMF) 算法.
- 应用NMF来学习面部部分和文本的语义特征.
- 将NMF与主要成分分析 (PCA) 和矢量量化 (VQ) 进行比较.
主要成果:
- NMF成功地学习了面部和文本的基于部分的表示.
- 与PCA和VQ相比,NMF产生了整体的表示.
- 在NMF中的非负性约束使得添加剂,基于部分的组合成为可能.
结论:
- 非负矩阵分解为学习基于部分的表示提供了一种方法.
- 非负性约束是实现基于部分的表示的关键.
- 实现NMF作为一个具有非负射率和突触强度的神经网络,自然会产生基于部分的表示.
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