Scalable Graph Hashing with Feature Transformation Motivation Contribution How to utilize the whole graph and simultaneously avoid O(n2) complexity? Scalable graph hashing (SGH): A feature transformation(Shrivastava and Li,2014)method to effectively approximate the whole graph without explicitly computing it. oA sequential method for bit-wise complementary learning. Linear complexity. oOutperform state of the art in terms of both accuracy and scalability. 口卡得三·4元互Q0 Li (http://cs.nju.edu.cn/lwj) Learning to Hash LAMDA,CS.NJU 21 /43Scalable Graph Hashing with Feature Transformation Motivation Contribution How to utilize the whole graph and simultaneously avoid O(n 2 ) complexity? Scalable graph hashing (SGH): A feature transformation (Shrivastava and Li, 2014) method to effectively approximate the whole graph without explicitly computing it. A sequential method for bit-wise complementary learning. Linear complexity. Outperform state of the art in terms of both accuracy and scalability. Li (http://cs.nju.edu.cn/lwj) Learning to Hash LAMDA, CS, NJU 21 / 43