Comparison of PCA approaches for very large group ICA.
Comparison of PCA approaches for very large group ICA.
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DOI：
10.1016/j.neuroimage.2015.05.047
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发表时间：
2015-09
期刊：
影响因子：
5.7
通讯作者：
Rachakonda S
中科院分区：
文献类型：
--
作者：
Calhoun VD;Silva RF;Adalı T;Rachakonda S
大数据集在功能磁共振成像中越来越常见，随着更快脉冲序列的出现，通过主成分分析（PCA）进行数据缩减的内存有效策略变得非常有用，特别是对于广泛使用的方法，如组独立成分分析（伊卡）。在这篇评论中，我们讨论的结果和局限性，从最近的一篇论文的主题，并试图提供一个更完整的视角，可用的方法，以及讨论各种问题，考虑有关大组PCA组ICA。我们还提供了一个分析的计算时间，内存使用，和各种方法的数据负载的数量在多个场景下的小和非常大的数据集。
Large data sets are becoming more common in fMRI and, with the advent of faster pulse sequences, memory efficient strategies for data reduction via principal component analysis (PCA) turn out to be extremely useful, especially for widely used approaches like group independent component analysis (ICA). In this commentary, we discuss results and limitations from a recent paper on the topic and attempt to provide a more complete perspective on available approaches as well as discussing various issues to consider related to large group PCA for group ICA. We also provide an analysis of computation time, memory use, and number of dataloads for a variety of approaches under multiple scenarios of small and extremely large data sets.