# Learning to Hash for Indexing Big Data - A Survey

arXiv: https://arxiv.org/abs/1509.05472 · HTML: https://mlsanity.com/paper/1509.05472/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2015.

## Built on by (most cited first, 30 of 39)

- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [Efficient Passage Retrieval with Hashing for Open-domain Question Answering](https://mlsanity.com/paper/2106.00882/) (2021)
- [One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective](https://mlsanity.com/paper/2109.14449/) (2021)
- [Multiplying Matrices Without Multiplying](https://mlsanity.com/paper/2106.10860/) (2021)
- [Learning-Augmented k-means Clustering](https://mlsanity.com/paper/2110.14094/) (2021)
- [Triangle and Four Cycle Counting with Predictions in Graph Streams](https://mlsanity.com/paper/2203.09572/) (2022)
- [Worst-case Performance of Popular Approximate Nearest Neighbor Search Implementations: Guarantees and Limitations](https://mlsanity.com/paper/2310.19126/) (2023)
- [Item Recommendation from Implicit Feedback](https://mlsanity.com/paper/2101.08769/) (2021)
- [TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s](https://mlsanity.com/paper/2206.14286/) (2022)
- [Learning-based Support Estimation in Sublinear Time](https://mlsanity.com/paper/2106.08396/) (2021)
- [(Optimal) Online Bipartite Matching with Degree Information](https://mlsanity.com/paper/2110.11439/) (2021)
- [Accelerating Code Search with Deep Hashing and Code Classification](https://mlsanity.com/paper/2203.15287/) (2022)
- [Improved Frequency Estimation Algorithms with and without Predictions](https://mlsanity.com/paper/2312.07535/) (2023)
- [Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval](https://mlsanity.com/paper/2202.03384/) (2022)
- [LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes](https://mlsanity.com/paper/2106.01487/) (2021)
- [Learning to Hash Robustly, Guaranteed](https://mlsanity.com/paper/2108.05433/) (2021)
- [Unsupervised Multi-Index Semantic Hashing](https://mlsanity.com/paper/2103.14460/) (2021)
- [Nearest neighbor search with compact codes: A decoder perspective](https://mlsanity.com/paper/2112.09568/) (2021)
- [Unsupervised Hashing with Semantic Concept Mining](https://mlsanity.com/paper/2209.11475/) (2022)
- [An Efficient Content-based Time Series Retrieval System](https://mlsanity.com/paper/2310.03919/) (2023)
- [PHPQ: Pyramid Hybrid Pooling Quantization for Efficient Fine-Grained Image Retrieval](https://mlsanity.com/paper/2109.05206/) (2021)
- [HyP2 Loss: Beyond Hypersphere Metric Space for Multi-label Image Retrieval](https://mlsanity.com/paper/2208.06866/) (2022)
- [Deep Hashing with Semantic Hash Centers for Image Retrieval](https://mlsanity.com/paper/2507.08404/) (2025)
- [Sobolev neural network with residual weighting as a surrogate in linear and non-linear mechanics](https://mlsanity.com/paper/2407.16466/) (2024)
- [Meta Cross-Modal Hashing on Long-Tailed Data](https://mlsanity.com/paper/2111.04086/) (2021)
- [Unsupervised Space Partitioning for Nearest Neighbor Search](https://mlsanity.com/paper/2206.08091/) (2022)
- [CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing](https://mlsanity.com/paper/2210.04288/) (2022)
- [SCB-Dataset: A Dataset for Detecting Student and Teacher Classroom Behavior](https://mlsanity.com/paper/2304.02488/) (2023)
- [Probabilistic Approach to Black-Box Binary Optimization with Budget Constraints: Application to Sensor Placement](https://mlsanity.com/paper/2406.05830/) (2024)
- [Online Hashing with Similarity Learning](https://mlsanity.com/paper/2108.02560/) (2021)

## Abstract (arXiv)

The explosive growth in big data has attracted much attention in designing efficient indexing and search methods recently. In many critical applications such as large-scale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem. However, the straightforward solution using exhaustive comparison is infeasible due to the prohibitive computational complexity and memory requirement. In response, Approximate Nearest Neighbor (ANN) search based on hashing techniques has become popular due to its promising performance in both efficiency and accuracy. Prior randomized hashing methods, e.g., Locality-Sensitive Hashing (LSH), explore data-independent hash functions with random projections or permutations. Although having elegant theoretic guarantees on the search quality in certain metric spaces, performance of randomized hashing has been shown insufficient in many real-world applications. As a remedy, new approaches incorporating data-driven learning methods in development of advanced hash functions have emerged. Such learning to hash methods exploit information such as data distributions or class labels when optimizing the hash codes or functions. Importantly, the learned hash codes are able to preserve the proximity of neighboring data in the original feature spaces in the hash code spaces. The goal of this paper is to provide readers with systematic understanding of insights, pros and cons of the emerging techniques. We provide a comprehensive survey of the learning to hash framework and representative techniques of various types, including unsupervised, semi-supervised, and supervised. In addition, we also summarize recent hashing approaches utilizing the deep learning models. Finally, we discuss the future direction and trends of research in this area.