An improved parameter learning methodology for RVFL based on pseudoinverse learners

As a compact and effective learning model, the random vector functional link neural network (RVFL) has been confirmed with universal approximation capabilities. It has gained considerable attention in various fields. However, the randomly generated parameters in RVFL often lead to the loss of valid information and data redundancy, which severely degrades the model performance in practice. This paper first proposes an efficient network parameters learning approach for the original RVFL with pseudoinverse learner (RVFL-PL). Instead of taking the random feature mapping directly, RVFL-PL adopts a non-iterative manner to obtain influential enhancement nodes implanted with valuable information from input data, which realizes to improve the quality of the enhancement nodes and ease the problem caused by the randomly assigned parameters in the standard RVFL. Since the network parameters are optimized analytically, this improved variant can maintain the efficiency of the standard RVFL. Further, the RVFL-PL is extended to a multilayered structure (mRVFL-PL) to obtain high-level representations from the input data. The results of comprehensive experiments on some benchmarks indicate the performance improvement of the proposed method compared to other corresponding methods.