  Deep neural networks (DNNs) form the backbone of almost every
state-of-the-art technique in the fields such as computer vision, speech
processing, and text analysis. The recent advances in computational technology
have made the use of DNNs more practical. Despite the overwhelming performances
by DNN and the advances in computational technology, it is seen that very few
researchers try to train their models from the scratch. Training of DNNs still
remains a difficult and tedious job. The main challenges that researchers face
during training of DNNs are the vanishing/exploding gradient problem and the
highly non-convex nature of the objective function which has up to million
variables. The approaches suggested in He and Xavier solve the vanishing
gradient problem by providing a sophisticated initialization technique. These
approaches have been quite effective and have achieved good results on standard
datasets, but these same approaches do not work very well on more practical
datasets. We think the reason for this is not making use of data statistics for
initializing the network weights. Optimizing such a high dimensional loss
function requires careful initialization of network weights. In this work, we
propose a data dependent initialization and analyze its performance against the
standard initialization techniques such as He and Xavier. We performed our
experiments on some practical datasets and the results show our algorithm's
superior classification accuracy.
