An Experimental Study of Multi-Layer Multi-Valued Neural Network
Joshua Bassey, Xiangfang Li, Lijun Qian · 2019
Complex numbers are used to represent data in many practical applications such as in telecommunications, image processing, and speech recognition. In this work, we examine the efficiency of complex-valued neural networks and compare that with their real-valued counterpart. Specifically, we examine the performance of neural network with Multi Layer Multi-Valued Neuron (MLMVN) for classification on several benchmark datasets such as Iris and MNIST datasets. It is shown that in applications where complex numbers occur naturally, complex-valued neural networks such as MLMVN network could offer advantages such as more efficient embedding and processing of information over their real-valued counterparts. It is also observed that complex-valued neural networks have a tendency of overfitting especially in applications involving large datasets. Potential solution to the overfitting problem has been discussed.