Name
Abstract | ||
|---|---|---|
Independent Component Analysis (ICA) is a popular method for extracting inde- pendent features from visual data. However, as a fundamentally linear technique, there is always nonlinear residual redundancy that is not captured by ICA. Hence there have been many attempts to try to create a hierarchical version of ICA, but so far none of the approaches have a natural way to apply them more than once. Here we show that there is a relatively simple technique that transforms the absolute val- ues of the outputs of a previous application of ICA into a normal distribution, to which ICA maybe applied again. This results in a recursive ICA algorithm that may be applied any number of times in order to extract higher order structure from previous layers. |
Year | Venue | DocType |
|---|---|---|
2006 | NIPS | Conference |
Citations | PageRank | References |
3 | 0.60 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Honghao Shan | 1 | 20 | 2.84 |
| Lingyun Zhang | 2 | 4 | 1.66 |
| Garrison W. Cottrell | 3 | 1397 | 286.59 |