1. A processor implemented method comprising: (a) receiving, via one or more hardware processors, multiple labelled images at a layer of multiple layers of a deep neural network; (b) pre-processing, via the one or more hardware processors, the multiple labelled images; (c) transforming, via the one or more hardware processors, the pre-processed labelled images based on a predetermined weight matrix to obtain feature representation of the pre-processed labelled images at the layer, wherein the feature representation comprise feature vectors and associated labels of the pre-processed labelled images; (d) determining, via the one or more hardware processors, kernel similarity between the feature vectors based on a predefined kernel function; (e) determining, via the one or more hardware processors, a Gaussian kernel matrix based on the determined kernel similarity; (f) computing, via the one or more hardware processors, an error function based on the predetermined weight matrix and the Gaussian kernel matrix; and (g) computing, via the one or more hardware processors, a weight matrix associated with the layer based on the error function and the predetermined weight matrix, thereby training the layer of the multiple layers.