Matrix factorization with a sigmoid-like loss control
Matrix factorization with a sigmoid-like loss control is a scholarly work, published in 2024 in ''Neurocomputing''. The main subjects of the publication include Multi-label classification, outlier, mathematics, artificial intelligence, computer science, matrix, sigmoid function, mean squared error, biological function, recommender system, matrix decomposition, benchmark, overfitting, machine learning, and algorithm. The authors design a sigmoid-like function to control the loss of each individual prediction, which has two advantages.