Taming dimensionality in big neuroimaging data: Efficient orthonormal projective NMF via stochastic learning and data compression
A stochastic optimization strategy that processes mini-batches of the data, substantially improving scalability and accelerating computation through random data compression and leverage repulsive point processes to diversify mini-batches, reducing redundancy and the variance of updates is introduced.