An incremental linear-time learning algorithm for the Optimum-Path Forest classifier
arXiv:1604.03346v58 citations
AI Analysis
This is an incremental improvement for machine learning practitioners needing faster updates in classification tasks.
The paper tackles the problem of efficiently updating the Optimum-Path Forest classifier with new data, achieving linear-time incremental learning while maintaining similar accuracy compared to the original quadratic-time model.
We present a classification method with incremental capabilities based on the Optimum-Path Forest classifier (OPF). The OPF considers instances as nodes of a fully-connected training graph, arc weights represent distances between two feature vectors. Our algorithm includes new instances in an OPF in linear-time, while keeping similar accuracies when compared with the original quadratic-time model.