Learning to Hash Outline Introduction ②Learning to Hash o Isotropic Hashing Scalable Graph Hashing with Feature Transformation o Supervised Hashing with Latent Factor Models o Column Sampling based Discrete Supervised Hashing o Deep Supervised Hashing with Pairwise Labels Supervised Multimodal Hashing with SCM Multiple-Bit Quantization Distributed Learning Coupled Group Lasso for Web-Scale CTR Prediction Distributed Power-Law Graph Computing Stochastic Learning Fast Asynchronous Parallel Stochastic Gradient Descent Distributed Stochastic ADMM for Matrix Factorization Conclusion 口卡得，三4元互Q0 Li (http://cs.nju.edu.cn/lvj) Big Leaming CS.NJU 9/115Learning to Hash Outline 1 Introduction 2 Learning to Hash Isotropic Hashing Scalable Graph Hashing with Feature Transformation Supervised Hashing with Latent Factor Models Column Sampling based Discrete Supervised Hashing Deep Supervised Hashing with Pairwise Labels Supervised Multimodal Hashing with SCM Multiple-Bit Quantization 3 Distributed Learning Coupled Group Lasso for Web-Scale CTR Prediction Distributed Power-Law Graph Computing 4 Stochastic Learning Fast Asynchronous Parallel Stochastic Gradient Descent Distributed Stochastic ADMM for Matrix Factorization 5 Conclusion Li (http://cs.nju.edu.cn/lwj) Big Learning CS, NJU 9 / 115