Introduction Existing Methods (Unimodal)Supervised(semi-supervised)Methods Class labels or pairwise constraints: SSH:semi-supervised hashing(SSH)exploits both labeled data and unlabeled data for hash function learning(Wang et al.,2010a,b). MLH:minimal loss hashing(MLH)based on the latent structural SVM framework(Norouzi and Fleet,2011). KSH:kernel-based supervised hashing (Liu et al.,2012) LDAHash:linear discriminant analysis based hashing (Strecha et al., 2012) o LFH:supervised hashing with latent factor models(Zhang et al., 2014) o etc. Triplet-based methods: oHamming distance metric learning (HDML)(Norouzi et al.,2012) Column generation base hashing(CGHash)(Li et al.,2013) Li (http://cs.nju.edu.cn/lvj) Learning to Hash LAMDA,CS.NJU 13/43Introduction Existing Methods (Unimodal) Supervised (semi-supervised) Methods Class labels or pairwise constraints: SSH: semi-supervised hashing (SSH) exploits both labeled data and unlabeled data for hash function learning (Wang et al., 2010a,b). MLH: minimal loss hashing (MLH) based on the latent structural SVM framework (Norouzi and Fleet, 2011). KSH: kernel-based supervised hashing (Liu et al., 2012) LDAHash: linear discriminant analysis based hashing (Strecha et al., 2012) LFH: supervised hashing with latent factor models (Zhang et al., 2014) etc. Triplet-based methods: Hamming distance metric learning (HDML) (Norouzi et al., 2012) Column generation base hashing (CGHash) (Li et al., 2013) Li (http://cs.nju.edu.cn/lwj) Learning to Hash LAMDA, CS, NJU 13 / 43