Department of Physics, University of California, Santa Barbara, California, United States of America.
PLoS One. 2012;7(11):e50268. doi: 10.1371/journal.pone.0050268. Epub 2012 Nov 28.
We introduce a method for spatiotemporal data fusion and demonstrate its performance on three constructed data sets: one entirely simulated, one with temporal speech signals and simulated spatial images, and another with recorded music time series and astronomical images defining the spatial patterns. Each case study is constructed to present specific challenges to test the method and demonstrate its capabilities. Our algorithm, BICAR (Bidirectional Independent Component Averaged Representation), is based on independent component analysis (ICA) and extracts pairs of temporal and spatial sources from two data matrices with arbitrarily different spatiotemporal resolution. We pair the temporal and spatial sources using a physical transfer function that connects the dynamics of the two. BICAR produces a hierarchy of sources ranked according to reproducibility; we show that sources which are more reproducible are more similar to true (known) sources. BICAR is robust to added noise, even in a "worst case" scenario where all physical sources are equally noisy. BICAR is also relatively robust to misspecification of the transfer function. BICAR holds promise as a useful data-driven assimilation method in neuroscience, earth science, astronomy, and other signal processing domains.
我们介绍了一种时空数据融合方法，并在三个构建的数据集中展示了其性能：一个完全模拟的数据集，一个具有时间语音信号和模拟空间图像的数据集，以及另一个具有记录音乐时间序列和定义空间模式的天文图像的数据集。每个案例研究都是为了呈现特定的挑战，以测试该方法并展示其功能。我们的算法 BICAR（双向独立成分平均表示）基于独立成分分析（ICA），并从具有任意不同时空分辨率的两个数据矩阵中提取出时间和空间源对。我们使用物理传递函数来配对时间和空间源，该传递函数连接了两个源的动力学。BICAR 根据可重复性对源进行分级；我们表明，可重复性更高的源与真实（已知）源更相似。BICAR 对添加的噪声具有鲁棒性，即使在所有物理源噪声相等的“最坏情况”场景下也是如此。BICAR 对于传递函数的误指定也具有相对鲁棒性。BICAR 有望成为神经科学、地球科学、天文学和其他信号处理领域中一种有用的数据驱动同化方法。
