1 Introduction Problem Definition Existing Methods 2 Scalable Graph Hashing with Feature Transformation Motivation Model and Learning Experiment 3 Conclusion 4 Reference

1 Introduction Problem Definition Existing Methods 2 Isotropic Hashing 3 Supervised Hashing with Latent Factor Model 4 Supervised Multimodal Hashing with SCM 5 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 6 Conclusion 7 Reference

 Motivation – Why do we need big data integration? – How has “small” data integration been done? – Challenges in big data integration  Schema alignment  Record linkage  Data fusion  Emerging topics

1 Introduction Problem Definition Existing Methods 2 Isotropic Hashing Model Learning Experiment 3 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 4 Conclusion 5 Reference

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 AsySVRG 3 SCOPE 4 Conclusion

【人工智能】面向调线调坡的点云大数据分析及深度模型研究

信息化发展趋势 技术促进教育创新发展 “粤教云”应用实例分析

【机器学习】大数据背景下高校招生策略预测

《社会科学研究方法》课程教学资源（阅读材料）计算机学报：网络大数据——现状与展望