《大数据 Big Data》课程教学资源（参考文献）Learning to Hash for Big Data - A Tutorial

Learning to Hash for Big Data:A Tutorial 李武军 LAMDA Group 南京大学计算机科学与技术系 软件新技术国家重，点实验室 Nov29,2015 日卡*2元至Q0 Li (http://cs.nju.edu.cn/lvj) Learning to Hash CS.NJU 1/210

Learning to Hash for Big Data: A Tutorial o…
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Outline Introduction ② Unsupervised Hashing Supervised Hashing Ranking-based Hashing Multimodal Hashing 6 Deep Hashing Quantization Conclusion ⑨ Reference 日卡回24元，互Q0 Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS.NJU 2 /210

Outline 1 Introduction 2 Unsupervised Hashing 3 Supervised Hashing 4 Ranking-based Hashing 5 Multimodal Hashing 6 Deep Hashing 7 Quantization 8 Conclusion 9 Reference Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS, NJU 2 / 210

Introduction Outline Introduction Unsupervised Hashing Supervised Hashing Ranking-based Hashing Multimodal Hashing Deep Hashing Quantization Conclusion Reference 日卡三4元，互Q0 Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS.NJU 3/210

Introduction Outline 1 Introduction 2 Unsupervised Hashing 3 Supervised Hashing 4 Ranking-based Hashing 5 Multimodal Hashing 6 Deep Hashing 7 Quantization 8 Conclusion 9 Reference Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS, NJU 3 / 210

Introduction Nearest Neighbor Search (Retrieval) Given a query point g,return the points closest(similar)to g in the database (e.g.,image database). oUnderlying many machine learning,data mining,information retrieval problems. 口卡+得，二4元互)Q0 Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS.NJU 4 /210

Introduction Nearest Neighbor Search (Retrieval) Given a query point q, return the points closest (similar) to q in the database (e.g., image database). Underlying many machine learning, data mining, information retrieval problems. Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS, NJU 4 / 210

Introduction Big Data Big data has attracted much attention from both academia and industry. Facebook:750 million users Flickr:6 billion photos Wal-Mart:267 million items/day;4PB data warehouse oSloan Digital Sky Survey:New Mexico telescope captures 200 GB image data/day Science FOURTH PARADIGM data Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS.NJU 5 /210

Introduction Big Data Big data has attracted much attention from both academia and industry. Facebook: 750 million users Flickr: 6 billion photos Wal-Mart: 267 million items/day; 4PB data warehouse Sloan Digital Sky Survey: New Mexico telescope captures 200 GB image data/day Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS, NJU 5 / 210

Introduction Nearest Neighbor Search(NNS)for Big Data Challenge in big data applications: o Curse of dimensionality ●Storage cost ●Query speed 日卡三4元，互Q0 Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS.NJU 6 /210

Introduction Nearest Neighbor Search (NNS) for Big Data Challenge in big data applications: Curse of dimensionality Storage cost Query speed Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS, NJU 6 / 210

Introduction Similarity Preserving Hashing h(Statue of Liberty)= h(Napoleon)= h (Napoleon)= 10001010 01100001 011001Q1 flipped bit Should be very different Should be similar 0Q0 Li (http://cs.nju.edu.cn/lvj) Learning to Hash CS.NJU 7/210

Introduction Similarity Preserving Hashing Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS, NJU 7 / 210

Introduction Reduce Dimensionality and Storage Cost Gist vector Binary reduction 10 million images 20 GB 160MB 口卡+得二4元互)Q0 Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS.NJU 8/210

Introduction Reduce Dimensionality and Storage Cost Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS, NJU 8 / 210

Introduction Fast Query Speed o By using hash-code to construct index,we can achieve constant or sub-linear search time complexity. o In some cases,exhaustive search with linear time complexity is also acceptable because the distance calculation cost is low with binary representation. 日卡三4元，互Q0 Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS.NJU 9/210

Introduction Fast Query Speed By using hash-code to construct index, we can achieve constant or sub-linear search time complexity. In some cases, exhaustive search with linear time complexity is also acceptable because the distance calculation cost is low with binary representation. Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS, NJU 9 / 210

Introduction Two Stages of Hash Function Learning Two main categories: oCategory I: Projection Stage (Dimension Reduction) Projected with real-valued projection function o Given a point x,each projected dimension i will be associated with a real-valued projection function fi(x)(e.g.,fi(x)=wx) Quantization Stage Turn real into binary o Essential difference between metric learning and learning to hash Category Il: Binary-Code Learning Stage Hash Function Learning Stage 日卡三4元，互Q0 Li (http://cs.nju.edu.cn/lvj) Learning to Hash CS.NJU 10 /210

Introduction Two Stages of Hash Function Learning Two main categories: Category I: Projection Stage (Dimension Reduction) Projected with real-valued projection function Given a point x, each projected dimension i will be associated with a real-valued projection function fi(x) (e.g., fi(x) = wT i x) Quantization Stage Turn real into binary Essential difference between metric learning and learning to hash Category II: Binary-Code Learning Stage Hash Function Learning Stage Li (http://cs.nju.edu.cn/lwj) Learning to Hash CS, NJU 10 / 210