reduces the overall dimensionality of input matrix to a lower dimensional space, where each consecutive dimension represents the large degree of variability.
reduces the overall dimensionality of input matrix to a lower dimensional space, where each consecutive dimension represents the large degree of variability.
reduce dimensionality of the problem resulting in a small, fast model with a richer, denser neighbour network