A method that splits mixed signals into independent hidden parts.
ICA is like hearing a call in a wild kitchen. The blender screams, the dog barks, and Dad sings off-key. ICA tries to give each sound its own lane.
It finds hidden sources in mixed data. You meet it in voice cleanup. You also meet it with brain waves and images.
Unsupervised Learning
ICA uses no labels and finds hidden structure in the data.
PCA
PCA looks for uncorrelated parts. ICA looks for independent parts.
Dim. Reduction
ICA can turn mixed signals into clearer feature form.
Latent Model
ICA treats independent parts as hidden variables behind the data.