具有潜在表示和动态图约束的多标签特征选择

Multi-label Feature Selection with Latent Representation and Dynamic Graph Constraints

针对现有嵌入式方法忽略实例相关性的潜在表示对伪标记学习的影响以及固定的图矩阵导致计算误差随迭代的加深而不断增大的问题,提出一种具有潜在表示和动态图约束的多标签特征选择方法.该方法首先利用实例相关性的潜在表示构造伪标签矩阵,并将其与线性映射和最小化伪标签与真实标签之间的Friedman范数距离相结合,从而保证伪标签与真实标签之间具有较高的相似性.其次,利用伪标签的低维流形结构构建动态图,以缓解固定图矩阵导致的随迭代深度增加计算误差的问题.在12个数据集上与7种先进方法的对比实验结果表明,该方法的整体分类性能优于现有先进方法,能较好地处理多标记特征选择问题.

Aiming at the problems that ignored by the existing embedded methods:the influence of the latent representation of instance correlation on pseudo-label learning,and the calculation error was caused by the fixed graph matrix,which increased with the deepening of iterations.We proposed a multi-label feature selection method with latent representation and dynamic graph constraints.Firstly,the proposed method used the latent representation of instance correlation to construct the pseudo-label matrix,and combined it with linear mapping and m inimizing the Friedman norm distance between the pseudo-label and the ground-truth label to ensure a high similarity between pseudo-labels and the ground-truth labels.Secondly,the dynamic graph was constructed by using the low-dimensional manifold structure of pseudo-labels to alleviate the problem of increasing calculation error with iteration depth caused by a fixed graph matrix.The comparative experimental results with seven advance methods on 12 datasets show that the overall classification performance of the proposed method is superior to the existing advanced methods,and it can better deal with multi-label feature selection problems.