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摘要翻译：
针对盲子空间反卷积(BSSD)问题，我们提出了一种新的求解方法。在盲子空间反卷积问题中，观测到多维隐藏独立分量的时间卷积，任务是仅利用观测发现隐藏分量。我们针对欠完全情况（uBSSD）进行了这一任务：我们将原来的uBSSD任务通过线性预测简化为独立子空间分析(ISA)来解决。最近的研究表明，应用时间级联也可以将uBSSD简化为ISA，但相关的ISA问题很容易变成高维问题[1]。新的约简方法规避了这一维数问题。我们通过数值模拟对所提出的技术的效率进行了详细的研究。我们发现了几个优点：我们的方法可以在较少的样本数下获得高质量的估计，并且可以处理较深的时间卷积。
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英文标题：
《Undercomplete Blind Subspace Deconvolution via Linear Prediction》
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作者：
Zoltan Szabo, Barnabas Poczos, Andras Lorincz
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最新提交年份：
2007
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
We present a novel solution technique for the blind subspace deconvolution (BSSD) problem, where temporal convolution of multidimensional hidden independent components is observed and the task is to uncover the hidden components using the observation only. We carry out this task for the undercomplete case (uBSSD): we reduce the original uBSSD task via linear prediction to independent subspace analysis (ISA), which we can solve. As it has been shown recently, applying temporal concatenation can also reduce uBSSD to ISA, but the associated ISA problem can easily become `high dimensional' [1]. The new reduction method circumvents this dimensionality problem. We perform detailed studies on the efficiency of the proposed technique by means of numerical simulations. We have found several advantages: our method can achieve high quality estimations for smaller number of samples and it can cope with deeper temporal convolutions.
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PDF链接：
https://arxiv.org/pdf/706.3435
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