Abstract
In this paper, the prospects and potentials of Convolutional Deep Independent Component Analysis are analyzed. Convolutional Deep Independent Component Analysis, abbreviate as CDICA, is an technique of incorporating Independent Component Analysis(ICA) with Fully Connected Neural Networks(FCNN). CDICA aims to simulate the convolutions similar the Convolutional Neural Network(CNN) with less time, less hyperparameters, and much smaller training samples. The simulation of convolutions are done by ICA algorithms and the feature merging step. The validity of CDICA are tested in this proposal.