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  1. Sciences
  2. Machine Learning
  3. Unsupervised Learning
  4. Dimensionality Reduction

Independant Component Analysis

PreviousDimensionality ReductionNextRandom Projection

Last updated 5 years ago

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PCA works to maximize variance, ICA assumes that the features are mixtures of independant sources. It tries to isolate these independant sources that are mixed in this dataset.

Ex: say you only have 3 recordings of a piano, cello and tv that were played but not playing the same musique:

With ICA, it is possible to retrieve each tune independantly.

Blind source separation

Algorithm

g is tanh function, but can be other functions, we have different options. The decorrelation matrix is W.

ICA Assumptions:

The components are statistically independant.

Components have non Gaussian distributions. Very important.

The algo tries to maximise the non gaussianity. One way to calculate this is the w+ above. It is called negentropy. w+ is how we approximate it.

Applications

Brain scan data, retail earnings to find patterns.