...  ...  @@ 136,7 +136,7 @@ We have already discussed a component analysis method, PCA. At this point, you m 





<img src="uploads/876dc45febab4a5593bcce12383aa455/ica_1.png" width="600">






In PCA, the way we extract the unit vectors of the new coordinate system relies on the variance. If we apply it to a problem composed on two independent phenomena, it will lead to a merged transformation, which is definitely wrong. In such cases, we need aim to filter out the these "independent" behaviors within the data.



In PCA, the way we extract the unit vectors of the new coordinate system relies on the variance. If we apply it to a problem composed on two independent phenomena, it will lead to a merged transformation, which is definitely wrong (see middle). In such cases, we first aim to filter out the these "independent" behaviors within the data (see right). So, how are we going to do that?









Note: In ICA, we assume that we do not have Gaussian distributions in the variables.

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