Introduction Existing Methods (Unimodal)Unsupervised Methods No labels to denote the categories of the training points. oPCAH:principal component analysis. o SH:eigenfunctions computed from the data similarity graph(Weiss etal.,2008). ITQ:orthogonal rotation matrix to refine the initial projection matrix learned by PCA(Gong and Lazebnik,2011). AGH:graph-based hashing (Liu et al.,2011). IsoHash:projected dimensions with isotropic variances(Kong and Li, 2012b). DGH:discrete graph hashing (Liu et al.,2014) o etc. 日卡三4元，互Q0 Li (http://cs.nju.edu.cn/lvj) Learning to Hash LAMDA,CS.NJU 12/43Introduction Existing Methods (Unimodal) Unsupervised Methods No labels to denote the categories of the training points. PCAH: principal component analysis. SH: eigenfunctions computed from the data similarity graph (Weiss et al., 2008) . ITQ: orthogonal rotation matrix to refine the initial projection matrix learned by PCA (Gong and Lazebnik, 2011) . AGH: graph-based hashing (Liu et al., 2011). IsoHash: projected dimensions with isotropic variances (Kong and Li, 2012b). DGH: discrete graph hashing (Liu et al., 2014) etc. Li (http://cs.nju.edu.cn/lwj) Learning to Hash LAMDA, CS, NJU 12 / 43