A Discriminative Correlation Platform for Feature Representation Learning in Multimodal Information Computing

The advancement of intelligent multimedia computing, particularly through deep neural networks (DNNs), has gained remarkable attention across a wide array of research domains and practical applications, such as audio-visual joint processing, image classification, video computing, computer vision, etc. However, the concern for sustainability and inherent black-box characteristics of DNNs have emerged as significant challenges, especially when dealing with multiple data sources. To tackle these challenges, alternative learning models have been actively explored in recent years, with green learning and interpretable learning standing out as two key directions. In this paper, a discriminative correlation perceptron-style neural network (DC-PNN) is proposed for feature representation learning with application to multimodal information computing. By leveraging the joint strength of statistics machine learning (SML) principles and the cascade architecture of a perceptron-style neural network (PNN), the DC-PNN model is green in nature with interpretable properties. To evaluate its effectiveness and generalizability, we conduct experiments on six benchmark databases with different scales and then apply to a real world problem, Glaucoma image classification. The results demonstrate that the DC-PNN model outperforms state-of-the-art (SOTA) methods. In addition, the parameters and FLOP analysis are implemented, validating the advantage of the model in terms of efficiency and resource requirements.