Joint Graph Based Embedding and Feature Weighting for Image Classification
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | Joint Graph Based Embedding and Feature Weighting for Image Classification |
| Type de publication | Conference Paper |
| Year of Publication | 2019 |
| Auteurs | Zhu R, Dornaika F, Ruichek Y |
| Conference Name | 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
| Publisher | IEEE |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-7281-1985-4 |
| Mots-clés | Feature selection, Graph-based embedding, image classification, semi-supervised learning, Supervised learning |
| Résumé | The graph-based embedding is an effective and useful method in reducing the dimension and extracting relevant data. This paper introduces a framework for classifying high dimensional data via a joint graph-based embedding and weighting method which could be used in semi-supervised or supervised learning. We design on effective optimization algorithm to solve the objective function. Experiments on image classification show that our proposed method can have a performance that is better than that of many state-of-the-art methods including linear and nonlinear methods. |