基于共享子空间的潜在语义学习
Journal of Nanjing University(Natural Sciences)(2022)
安庆师范大学
Abstract
在多视图多标签学习中,常使用子空间学习解决各视图间存在的异构性问题,而子空间提取一般通过降维的方法实现,但其映射到标签空间是一个从低维到高维的过程,易出现维度跨越问题.基于此,提出一种基于共享子空间的潜在语义学习方法,即预测过程变成从低维空间到低维空间的等维映射.首先,在学习多视图多标签数据的共享子空间的基础上,从标签空间中提取标签的潜在语义和系数矩阵;其次,共享子空间和原始标签空间分别约束潜在语义空间;最后,将共享子空间、标签潜在语义矩阵放入MLRKELM分类器进行学习.提出的算法在多个基准多视图多标签数据集上与多个先进算法进行了对比实验,结果验证了该算法的有效性.
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