1 Introduction Problem Definition Existing Methods Motivation and Contribution 2 Isotropic Hashing Model Learning Experimental Results 3 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 4 Conclusion 5 Reference

1 Introduction 2 Unsupervised Hashing 3 Supervised Hashing 4 Ranking-based Hashing 5 Multimodal Hashing 6 Deep Hashing 7 Quantization 8 Conclusion 9 Reference

1 Introduction 2 AsySVRG 3 SCOPE 4 Conclusion

◼ Parallel DBMS technologies ◆ Proposed in the late eighties ◆ Matured over the last two decades ◆ Multi-billion dollar industry: Proprietary DBMS Engines ◆ intended as Data Warehousing solutions for very large enterprises ◼ Hadoop ◼ Spark ◆ UC Berkeley

电子科技大学：《大数据分析与挖掘 Big Data Analysis and Mining》课程教学资源（课件讲稿）Lecture 2 BasicConcepts（Foundations of Data Mining）

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

1 Introduction 2 Learning to Hash Isotropic Hashing Supervised Hashing with Latent Factor Models 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 Distributed Stochastic ADMM

广西师范大学出版社：《大数据 The Big Data Revolution》PDF电子书（著：涂子沛）

同济大学：《大数据分析与数据挖掘 Big Data Analysis and Mining》课程教学资源（PPT课件讲稿）关联规则 Association Rule

《人工智能、机器学习与大数据》课程教学资源（参考文献）Learning to hash for big data - current status and future trends