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.

翻译：我们为盲子空间分解(BSSD)问题提出了一个新颖的解决办法,即观察多维隐性独立部件的时变,任务只是利用观察来发现隐藏部件。我们执行这一任务是为了处理不完全的个案(usBSSD):我们通过线性预测将最初的 usBSSD 任务减少为独立的子空间分析(ISA),这是我们可以解决的。正如最近所显示的那样,应用时间共解也可以减少ISA的 usSD,但相关的ISA 问题很容易成为“高维”[1]。新的减少方法绕过这一维度问题。我们通过数字模拟方法对拟议技术的效率进行了详细研究。我们发现了几个优点:我们的方法可以对少量样品进行高质量的估计,并且能够应对更深的时间相变。