独立成分分析
自编码
组分(热力学)
非线性系统
计算机科学
模式识别(心理学)
应用数学
数学
算法
人工智能
人工神经网络
物理
量子力学
热力学
作者
Tianwen Wei,Stéphane Chrétien
标识
DOI:10.1109/icassp.2019.8682469
摘要
We propose Independent Component Autoencoder (ICAE), a deep neural network-based framework for nonlinear Independent Component Analysis (ICA). The proposed method consists of a penalized autoencoder and a training objective that is to minimize a combination of the reconstruction loss and an ICA contrast. Unlike many previous ICA methods that are usually tailored to separate specific mixture, our method can recover sources from various mixtures, without prior knowledge on the nature of that mixture.
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