Fast learning for big data applications using parameterized multilayer perceptron

An innovative approach has been proposed for using MLP for handling Big data. There is high computational cost and time involved in using MLP for classification of Big data having large number of features. A parameterized multilayer perceptron (PMLP) has been proposed where the weight matrix has been parameterized using periodic functions. This ensures that the weight values are bounded which leads to inherent regularization. Memory requirements for storing the weight matrix is drastically reduced. This also leads to increase in classification accuracy associated with drastic reduction in computational time as compared to MLP when executed on large benchmark datasets. This is a promising technique for handling Big data.