联合嵌入式多标签分类算法

现有的一些多标签分类算法，因多标签数据含有高维的特征或标签信息而变得不可行.为了解决这一问题，提出基于去噪自编码器和矩阵分解的联合嵌入多标签分类算法Deep AE-MF.该算法包括两部分:特征嵌入部分使用去噪自编码器对特征空间学习得到非线性表示，标签嵌入部分则是利用矩阵分解直接学习到标签空间对应的潜在表示与解码矩阵.Deep AE-MF将特征嵌入和标签嵌入的两个阶段进行联合，共同学习一个潜在空间用于模型预测，进而得到一个有效的多标签分类模型.为了进一步提升模型性能，在Deep AE-MF方法中对标签间的负相关信息加以利用.通过在不同数据集上进行实验证明了提出Deep AE-MF方法的有效性和鲁棒性.

A Joint Embedded Multi-label Classification Algorithm

Some existing classification algorithms become infeasible anymore, because most multi-label data contains high-dimensional features or label information. To solve this problem, a joint embedded multi-label learning classification algorithm named Deep AE-MF is proposed in this paper, which is based on denoising auto-encoder and matrix factorization. The algorithm includes two parts:the feature embedding part uses denoising auto-encoder to obtain the nonlinear representation of feature space learning, and the label embedding part directly learns the potential representation and decoding matrix of the corresponding label space using matrix factorization. In order to get an effective classification model, Deep AE-MF combines the two phases of feature embedding and label embedding to learn a potential space for model prediction. To further improve the performance of the model, the negative correlation between tags is exploited in Deep AE-MF. Experiments on different datasets show the effectiveness and robustness of the proposed Deep AE-MF method.