To Infinity and Beyond: On ICA over Hilbert Spaces
The original Independent Component Analysis (ICA) problem of blindly separating a mixture of a finite number of real-valued statistically independent one-dimensional sources has been extended in a number of ways in recent years. These include dropping the assumption that all sources are one-dimensional and some extensions to the case where the sources are not real-valued. We introduce an extension in a further direction, no longer assuming only a finite number of sources, but instead allowing infinitely many. We define a notion of independent sources for this case and show separability of ICA in this framework.
KeywordsHilbert Space Random Vector Independent Component Analysis Dimensional Case Real Hilbert Space
Unable to display preview. Download preview PDF.