Multi-label learning has been received much attention due to its applicability in machine learning problems. In current years, quite few approaches based on either extreme learning machine (ELM) or radial basis function (RBF) neural network have been proposed with the aim of increasing the efficiency of the multi-label classification. Most existing multi-label learning algorithms focus on information about the feature space. In this paper, our major intention is to regularize the objective function of multi-label learning methods via Locally Linear Embedding (LLE). To achieve this goal, two neural network architectures namely Multi-Label RBF (ML-RBF) and Multi-Label Multi Layer ELM (ML-ELM) are utilized. Then, a regularized multi-label learning method via feature manifold learning (RMLFM) and a regularized multi-label learning method via dual-manifold learning (RMLDM) are established for training two network structures. RMLDM simultaneously exploits the geometry structure of both feature and data space. Furthermore, eight different configurations of applying training algorithms (i.e., RMLFM and RMLDM) to model architectures (i.e., ML-RBF and ML-ELM) are considered for conducting comparisons. The validity and effectiveness of these eight classifiers are indicated by a number of experimental studies on several multi-label datasets. Furthermore, the experiments indicate that the efficiency of the classification can be improved considerably against some cutting-the-edge multi-label techniques for the neural classifiers in which the dual-manifold learning is used as the training method.

多标签学习由于其在机器学习问题中的适用性而备受关注。近年来，为了提高多标签分类的效率，已经提出了很少几种基于极限学习机（ELM）或径向基函数（RBF）神经网络的方法。大多数现有的多标签学习算法都专注于有关特征空间的信息。在本文中，我们的主要目的是通过局部线性嵌入（LLE）规范多标签学习方法的目标函数。为了实现此目标，使用了两种神经网络架构，即Multi-Label RBF（ML-RBF）和Multi-Label多层ELM（ML-ELM）。然后，建立了通过特征流形学习（RMLFM）的正则化多标签学习方法和通过双流形学习（RMLDM）的正则化多标签学习方法来训练两个网络结构。RMLDM同时利用要素空间和数据空间的几何结构。此外，考虑了将训练算法（即RMLFM和RMLDM）应用于模型架构（即ML-RBF和ML-ELM）的八种不同配置，以进行比较。这八个分类器的有效性和有效性由对多个多标签数据集的大量实验研究表明。此外，