Contrasting Convolutional Neural Network (CNN) with Multi-Layer Perceptron (MLP) for Big Data Analysis

Recently, CNNs have become very popular in the machine learning field, due to their high predictive power in classification problems that involve very high dimensional data with tens of hundreds of different classes. CNN is a natural extension to MLP with few modifications which resulted in a breakthrough. Mainly, the MLP algebraic dot product as a similarity function was replaced with 2-d convolution; in addition to a pooling layer which reduces parameter dimensions making the model equi-variant to translations, distortions, and transformations. The sparse connectivity nature of CNN is also a variation to the MLP. The two models were implemented on the EMNIST dataset which was used as 50% and 100% of its capacity. The models were trained with fixed and flexible number of epochs in two runs. Using 100% of EMNIST; for the fixed run CNN achieved test accuracy of 92% and MLP 31.43%, where in the flexible run the CNN achieved 92% and MLP 89.47%. Using 50% of EMNIST; for the fixed run CNN achieved test accuracy of 92.9% and MLP 33.75%, where in the flexible run of 92.9% and MLP 88.20%. The CNN demonstrated a good maintenance of high accuracy for image like inputs and also proved to be a better candidate for big data applications.