Progressive Learning Model for Big Data Analysis Using Subnetwork and Moore-Penrose Inverse
Wandong Zhang, Yimin Yang, Zeng Li, Q. M. Jonathan Wu · IEEE Transactions on Multimedia · 2024
Multilayer analytic learning plays a crucial role in data mining and representation learning. Nevertheless, most of them encounter inefficiencies in latent space encoding, resulting in less effective data representations. Aimed at addressing this limitation, this paper introduces two potent analytic learning methods, the progressive learning-based hierarchical subnet neural network (P-HSNN) and the robust P-HSNN (RP-HSNN). The contributions are as follows. First, two progressive learning astrategies based on subnetwork nodes are proposed. Second, the RP-HSNN is a Laplacian matrix-based algorithm, where label information and input representations are utilized simultaneously to optimize the subspace feature. Third, the dimension of subnetwork node is gradually increased. The global-level representation is formed by combining the features from the subnetworks. The model's convergence is thoroughly demonstrated through rigorous mathematical proof. Experimental analyses across various domains, spanning a wide range of training samples from 2,754 to 1,623,114, confirm the superior performance of the proposed algorithms over state-of-the-art multilayer analytic learning methods.