论文标题
使用最大值在相空间图中使用最大值的数据混合物估算稀疏源
Estimating Sparse Sources from Data Mixtures using Maxima in Phase Space Plots
论文作者
论文摘要
在盲源分离（BSS）中，一个人从混合系数未知的数据混合物中估计来源。在特定的稀疏组件分析（SCA）的情况下，每种基础源仅在其他来源可忽略不计时只有有限的时间。在本文中，提出了一种使用相位空间分析表示数据的方法，并且从相位图中的最大值表示主要来源。通缩用于估计其他来源。对所提出的方法进行了模拟数据和从期待母亲获取的实验性心电图数据的测试。结果表明，在大多数情况下，所提出的方法的性能与主成分分析（PCA）和FastICA的性能相当。对于嘈杂的数据，发现PCA对于较高的噪声水平更为强大。对于源具有一致峰的情况，该方法按预期分解，因为相位图中的最大值与单个源无关。
In Blind Source Separation (BSS), one estimates sources from data mixtures where the mixing coefficients are unknown. In the particular case of Sparse Component Analysis (SCA), each underlying source exists for only a finite amount of time when other sources are negligible. In this paper, one approach to SCA is presented where the data are represented using phase space analysis and one estimates the main source from the maximum in the phase plot. Deflation is used to estimate the other sources. The proposed method is tested on simulated data and experimental ECG data taken from an expectant mother. It is shown that, in most cases, the performance of the proposed method is comparable to that of Principal Component Analysis (PCA) and FastICA for clean data. In the case of noisy data, PCA is found to be more robust for higher noise levels. For situations where the sources have coincident peaks, the method breaks down as expected, as the maximum in the phase plot does not correspond to an individual source.
