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Large Scale Metric Learning from Equivalence Constraints (KISSME)
時間 2021-01-08
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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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