Joint Graph Based Embedding and Feature Weighting for Image Classification
Ruifeng Zhu, Fadi Dornaika, Yassine Ruichek · 2019
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.