Large Scale Metric Learning from Equivalence Constraints (KISSME)
一：介紹 現有的Mahalanobis度量學習方法很多是通過梯度下降來迭代更新M矩陣，監督程度較高（如需要所有樣本標籤的LMNN方法）和計算複雜（需要大量的迭代）對於樣本數目日益增長的大規模數據集是很不友好的。作者從概率的觀點，計算髮生概率的最大似然比率來計算樣本的馬氏距離，無需進行昂貴的迭代運算，而且僅需要樣本間yij=0或1 （即equivalence constraints）的監督信息，對於
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相關文章
- 1. Large-scale Distance Metric Learning with Uncertainty
- 2. Large Scale Image Classification
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- 9. Survey and experimental study on metric learning methods
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