Large Scale Metric Learning from Equivalence Constraints (KISSME)
一：介绍 现有的Mahalanobis度量学习方法很多是通过梯度下降来迭代更新M矩阵，监督程度较高（如需要所有样本标签的LMNN方法）和计算复杂（需要大量的迭代）对于样本数目日益增长的大规模数据集是很不友好的。作者从概率的观点，计算发生概率的最大似然比率来计算样本的马氏距离，无需进行昂贵的迭代运算，而且仅需要样本间yij=0或1 （即equivalence constraints）的监督信息，对于
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