Unfolded Self-reconstruction LSH: Towards Machine Unlearning In Approximate Nearest Neighbour Search

Abstract

Approximate nearest neighbour (ANN) search is an essential component of search engines, recommendation systems, etc. Many recent works focus on learning-based data-distribution-dependent hashing and achieve good retrieval performance. However, due to increasing demand for users' privacy and security, we often need to remove users' data information from Machine Learning (ML) models to satisfy specific privacy and security requirements. This need requires the ANN search algorithm to support fast online data deletion and insertion. Current learning-based hashing methods need retraining the hash function, which is prohibitable due to the vast time-cost of large-scale data. To address this problem, we propose a novel data-dependent hashing method named unfolded self-reconstruction locality-sensitive hashing (USR-LSH). Our USR-LSH unfolded the optimization update for instance-wise data reconstruction, which is better for preserving data information than data-independent LSH. Moreover, our US

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keytan2023unfolded

Related papers

• DET-LSH: A Locality-sensitive Hashing Scheme With Dynamic Encoding Tree For Approximate Nearest Neighbor Search (2024)9.92
• SLOSH: Set Locality Sensitive Hashing Via Sliced-wasserstein Embeddings (2021)5.24
• PM-LSH: A Fast And Accurate In-memory Framework For High-dimensional Approximate NN And Closest Pair Search (2021)8.09
• Improved Space-efficient Approximate Nearest Neighbor Search Using Function Inversion (2024)0.00
• A Revisit Of Hashing Algorithms For Approximate Nearest Neighbor Search (2016)11.19
• Experimental Analysis Of Locality Sensitive Hashing Techniques For High-dimensional Approximate Nearest Neighbor Searches (2020)6.34
• Experimental Analysis Of Machine Learning Techniques For Finding Search Radius In Locality Sensitive Hashing (2022)0.00
• Multi-level Spherical Locality Sensitive Hashing For Approximate Near Neighbors (2017)0.00