Manifold Regularization Classification Model Based On Improved Diffusion Map

arXiv:2403.16059v1 Announce Type: cross
Abstract: Manifold regularization model is a semi-supervised learning model that leverages the geometric structure of a dataset, comprising a small number of labeled samples and a large number of unlabeled samples, to generate classifiers. However, the original manifold norm limits the performance of models to local regions. To address this limitation, this paper proposes an approach to improve manifold regularization based on a label propagation model. We initially enhance the probability transition matrix of the diffusion …

abstract arxiv classification classification model classifiers cs.lg dataset diffusion generate however manifold map math.oc norm performance regularization samples semi-supervised semi-supervised learning small stat.ml supervised learning type