IEEE Trans Neural Netw Learn Syst. 2017 Dec;28(12):2949-2960. doi: 10.1109/TNNLS.2016.2609434. Epub 2016 Sep 28.
In this paper, we propose a novel graph-based semisupervised learning framework, called joint sparse representation and embedding propagation learning (JSREPL). The idea of JSREPL is to join EPL with sparse representation to perform label propagation. Like most of graph-based semisupervised propagation learning algorithms, JSREPL also constructs weights graph matrix from given data. Different from classical approaches which build weights graph matrix and estimate the labels of unlabeled data in sequence, JSREPL simultaneously builds weights graph matrix and estimates the labels of unlabeled data. We also propose an efficient algorithm to solve the proposed problem. The proposed method is applied to the problem of semisupervised image clustering using the ORL, Yale, PIE, and YaleB data sets. Our experiments demonstrate the effectiveness of our proposed algorithm.
在本文中，我们提出了一种新颖的基于图的半监督学习框架，称为联合稀疏表示和嵌入传播学习（JSREPL）。JSREPL 的思想是将 EPL 与稀疏表示相结合进行标签传播。与大多数基于图的半监督传播学习算法一样，JSREPL 也从给定数据中构建权重图矩阵。与经典方法不同，经典方法是依次构建权重图矩阵并估计未标记数据的标签，JSREPL 同时构建权重图矩阵并估计未标记数据的标签。我们还提出了一种有效的算法来解决所提出的问题。所提出的方法应用于使用 ORL、Yale、PIE 和 YaleB 数据集的半监督图像聚类问题。我们的实验证明了我们提出的算法的有效性。
