Introduction Problem Definition Hash Function Learning Easy or hard? Hard:discrete optimization problem Easy by approximation:two stages of hash function learning oProjection stage (dimensionality reduction) Projected with real-valued projection function Given a point x,each projected dimension i will be associated with a real-valued projection function fi(x)(e.g.fi(x)=wx) ●Quantization stage Turn real into binary However,there exist essential differences between metric learning(dimensionality reduction)and learning to hash.Simply adapting traditional metric learning is not enough. Li (http://cs.nju.edu.cn/lvj) Learning to Hash LAMDA.CS.NJU 9/43Introduction Problem Definition Hash Function Learning Easy or hard? Hard: discrete optimization problem Easy by approximation: two stages of hash function learning Projection stage (dimensionality reduction) Projected with real-valued projection function Given a point x, each projected dimension i will be associated with a real-valued projection function fi(x) (e.g. fi(x) = wT i x) Quantization stage Turn real into binary However, there exist essential differences between metric learning (dimensionality reduction) and learning to hash. Simply adapting traditional metric learning is not enough. Li (http://cs.nju.edu.cn/lwj) Learning to Hash LAMDA, CS, NJU 9 / 43