半监督度量学习内蕴最速下降算法的收敛性分析

摘要

In this paper,we derive the convergence problem of an intrinsic steepest descent algorithm for semi-supervised metric learning problem on symmetric positive definite matrices groups.We first rewrite semi-supervised metric learning problem into an unconstrained optimization problem on symmetric positive definite matrices groups.Then we present an intrinsic steepest descent algorithm with an adaptive iteration step-size.Moreover,we prove that the algorithm converges linearly by using a Taylor's expansion of smooth function at any point in Lie groups.Finally,we show a few numerical experiments on classification problem to demonstrate the effectiveness of the proposed algorithm.%主要研究对称正定矩阵群上的内蕴最速下降算法的收敛性问题.首先针对一个可转化为对称正定矩阵群上无约束优化问题的半监督度量学习模型,提出对称正定矩阵群上一种自适应变步长的内蕴最速下降算法.然后利用李群上的光滑函数在任意一点处带积分余项的泰勒展开式,证明所提算法在对称正定矩阵群上是线性收敛的.最后通过在分类问题中的数值实验说明算法的有效性.