Dimensionality reduction 101: linear algebra, hidden variables and generative models

Suppose you are faced with a high dimensional dataset and want to find some structure in the data: often there are only a few causes, but lots of different data points are generated due to noise corruption. How can we infer these causes? Here I’m going to cover the simplest method to do this inference: we will assume the data is generated by a linear transformation of the hidden causes. In this case, it is quite simple to recover the parameters of this transformation and therefore determine the hidden (or latent) variables which represent their cause.

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