We introduce the blind subspace deconvolution (BSSD) problem, which is the extension of both the blind source deconvolution (BSD) and the independent subspace analysis (ISA) tasks. We examine the case of the undercomplete BSSD (uBSSD). Applying temporal concatenation we reduce this problem to ISA. The associated `high dimensional' ISA problem can be handled by a recent technique called joint f-decorrelation (JFD). Similar decorrelation methods have been used previously for kernel independent component analysis (kernel-ICA). More precisely, the kernel canonical correlation (KCCA) technique is a member of this family, and, as is shown in this paper, the kernel generalized variance (KGV) method can also be seen as a decorrelation method in the feature space. These kernel based algorithms will be adapted to the ISA task. In the numerical examples, we (i) examine how efficiently the emerging higher dimensional ISA tasks can be tackled, and (ii) explore the working and advantages of the derived kernel-ISA methods.

翻译：我们引入了盲子空间分变变(BSD)问题,即盲源分变(BSD)和独立的子空间分析(ISA)任务的延伸。我们研究了未完全的BSD(UBSD)案例。应用时间共解,我们将这一问题降低到ISA。相关的“高维”ISA问题可以通过一个名为“联合装饰(JFD)”的最近技术来解决。以前曾使用类似的装饰方法来进行内核独立部件分析(内核-ICA)。更确切地说,内核相关(KCA)技术是这一家庭的一个成员,正如本文所示,内核普遍差异(KGV)方法也可以被视为地貌空间的一种解导法。这些内核算法将适应ISA的任务。在数字学中,我们(一)研究正在形成的更高维度的ISA任务能够如何有效地解决,以及(二)探索衍生的内核-ISA方法的工作和优势。