Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-28551-6_23 | LVA/ICA |
Keywords | Field | DocType |
finite number,show separability,independent one-dimensional source,original independent component analysis,recent year,hilbert space,independent source | Hilbert space,Discrete mathematics,Finite set,Infinity,Pure mathematics,Multivariate random variable,Independent component analysis,Mathematics,Independence (probability theory) | Conference |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harold W. Gutch | 1 | 45 | 4.60 |
| Fabian J. Theis | 2 | 931 | 85.37 |