The Gaussian mixture model is a parametric model that learns the underlying distribution of data. If data contains the noisy attributes and noisy samples then will the model learn that noisy data too or it learns only normal samples and attributes ??? Can we avoid noisy attributes without dimension reduction or feature selection
Is it possible to avoid noisy or redundant attributes while learning the distribution directly??
Here I'm assuming my data to be high dimensional which have redundant and erroneous attributes