Practical Near Neighbor Search via Group Testing

1 Introduction . Nearest neighbor search is a fundamental problem with many applications in machine learning systems . Informally , the task is as follows . Given a dataset D = { x1 , x2 , ... xN } , we wish to build a data structure that can be queried with any point q to obtain a small set of points xi ∈ D that have high similarity ( low distance ) to the query . This structure is called an index . Near neighbor indices form the backbone of production models in recommendation systems , social networks , genomics , computer vision and many other application domains . Applications : In this paper , we focus on algorithms for approximate near neighbor search over highdimensional large scale datasets . Such tasks frequently arise in genomics , web-scale data mining , machine learning , and other large-scale applications . Consider the Yahoo Flickr Creative Commons dataset ( YFCC100M ) which consists of 100 million media embeddings that are derived from the neuron activations for a convolutional neural network [ 21 ] . Each embedding is a 4096-dimensional vector . The dataset is about 1TB in size and presents a substantial challenge for even the most popular algorithms , which struggle with memory , index construction , and query time . Similar issues occur ∗Equal contribution . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . in genomics , where the task is to identify genome sequences with a high Jaccard similarity to the query . Modern genomic datasets can contain millions of reads with billions of possible n-gram sequences [ 17 ] . Many algorithms work well when there are a few hundred dimensions but are inappropriate for such applications . Our experiments demonstrate that for the datasets of interest in this paper , popular indices like HNSW and FAISS can take days to build , require gigabytes of RAM and have a suboptimal precision-recall-query time tradeoff . Since our goal is to perform approximate search , dimensionality reduction is a reasonable strategy . However , dimensionality reduction is costly for ultra-high dimensional data . In genomics applications , n-gram sizes are typically very large ( n > 18 ) . Thus , the one-hot encoding of each sequence can require billions of dimensions ( 418 ≈ 68B ) , making it intractable to learn an embedding model . For embedding applications such as YFCC100M or product search , a large embedding dimension can lead to performance improvements [ 15 ] . Dimensionality reduction can incur a performance penalty , so we may wish to perform the near neighbor search over the original metric space . Ideally , we would choose an algorithm that did not store data points in RAM , evaluate the distance function many times , employ iterative and non-streaming processes such as k-means , or construct complicated structures such as graphs , which are hard to parallelize and distribute . Recent algorithms such as FLASH [ 22 ] provide the ability to search based on aggregate LSH count statistics without computing distances , but these methods are heuristics that do not have theoretical guarantees . On the other hand , algorithms such as LSH , which have a well-established theoretical grounding , tend to perform poorly in practice because of their prohibitive hash table size and post filtering stage , which needs many ( Nρ in theory ) distance computations . In this paper , we present an algorithm having all the practical advantages of a system like FLASH while also being more accurate , theoretically sound , and provably sub-linear . 1.1 Our Contribution . In this paper , we address the computational challenges of high-dimensional similarity search by presenting an index with fast construction time , low memory requirement , and zero query-time distance computations . Our approach is to transform a near neighbor search problem into a group testing problem by designing a test that outputs “ positive ” when a group of points contains a near neighbor to a query . That is , each test answers an approximate membership query over its group . Given a query , our algorithm produces a B × R array of group test results that can be efficiently decoded to identify the nearest neighbors . This is more efficient than statistical aggregation algorithms like FLASH because each test filters out entire groups of non-neighbors with a single test operation . We develop a concrete example of such an algorithm by using Filters to Identify Near Neighbor Groups ( FLINNG ) . We use a standard non-adaptive group testing design with distance-sensitive Bloom filters as tests . We prove that FLINNG solves the randomized nearest neighbor problem in O ( log2 ( 1δ ) log 3 ( N ) N 1 2 +γ ) time , where γ is a query-dependent parameter that characterizes query stability . We also implement FLINNG in C++ and conduct experiments on real-world highdimensional datasets from genomics , embedding search , and URL analysis , where FLINNG achieves up to a 10x query speedup over existing indices with faster construction time and lower memory . 2 Related Work . The near neighbor problem has been the focus of more than four decades of intense research activity . The low dimensional problem is particularly well-understood , with space partitioning trees that can efficiently find the exact k-nearest neighbors . However , exact search in high dimensions is intractable due to the curse of dimensionality - the computational resources needed to solve the exact problem scale exponentially with dimensions . This has led to a diverse set of algorithms to solve the approximate near neighbor problem , which we now describe . Locality Sensitive Hashing : LSH was the first approximate near neighbour algorithm to break the curse of dimensionality . At their core , LSH algorithms use an LSH function to partition the dataset into buckets . The hash function is selected so that the distance between points in the same bucket is likely to be small . To find the near neighbors of a query , we hash the query and compute the distance to every point in the corresponding bucket . Query performance can be improved with replication , which queries multiple independent hash tables , multi-probe methods [ 13 ] , which examine multiple buckets in the hash table , and data-dependent LSH [ 3 ] , which tunes the hash function to the dataset . Recent work shows that machine learning algorithms can also construct effective LSH partitions [ 6 ] . Count-Based LSH : There are several recent algorithms which identify neighbors by counting the number of LSH collisions rather than explicitly computing distances . For example , the algorithm from [ 22 ] uses the count values to quickly identify potential neighbors . The algorithm from [ 5 ] applies compressed sensing techniques to the counts to compress the dataset , and a popular technique in genomics is to simply replace each data point with its hash values [ 17 ] . Graphs : Graph-based methods are another successful family of algorithms . Graph algorithms locate near neighbors by walking the edges of a graph where each point is ( approximately ) connected to its k nearest neighbors . The focus in this area has been to improve graph properties using diversification , pruning , hierarchical structures , and other heuristics [ 14 ] . Graph indices perform well on industrystandard benchmarks but are not theoretically well-understood , despite recent progress [ 18 ] . Graph indices also suffer from long construction times and bloated memory consumption . Sample Compression : A large number of practical methods are based on quantization . Such methods replace points in the dataset with compressed versions of the points . Methods such as scalar quantization , vector quantization and product quantization alias each point to a collection of k-means centroids . One can also use machine learning to obtain learned Hamming codes for the dataset and perform efficient distance computations using bit operations . Advances in quantization are applicable to most other algorithms , but have been particularly effective when combined with brute force search on GPU hardware and partition-based search over billion-scale datasets [ 11 ] . Group Testing : We are not the first algorithm to apply group testing to near neighbor search . However , existing algorithms have key limitations that prevent effective practical implementations and rigorous theoretical analysis . The authors of [ 10 ] propose a group-based filtering algorithm based on group representative vectors , or the vector average of group entries . To query the index , [ 10 ] explicitly compute the distances to all points where the distance between the representative and query exceeds a threshold . The algorithm of [ 20 ] uses the same group representatives , but applies an online backpropagation algorithm to estimate the individual similarities . This work has two shortcomings . First , the methods require many distance calculations against the group representatives ( [ 10 ] requires N/10 distances ) , resulting in poor query time . Second , the average vector can be similar to the query even when all points are far from the query , precluding a theoretical analysis except under restrictive distribution assumptions . In this work , we analyze methods where only the group tests are used to identify the neighbors , as our goal is to avoid performing expensive distance computations . Unsurprisingly , our method is theoretically and practically superior . Theoretical Comparison : In high dimensions , most methods require conditions that depend on the data and query to prove efficient search time . Table 1 shows some representative theoretical results for the classes of algorithms described previously . 3 Background . Formal Problem Statement : In this paper , we solve the randomized nearest neighbor problem . Definition 1 is a stronger version of the well-studied ( R , c ) -approximate near neighbor problem . In particular , any algorithm which solves the randomized nearest neighbor problem also solves the approximate near neighbor problem with c = 1 and any R ≥ the distance to the nearest neighbor . Definition 1 . Randomized Nearest neighbor : Given a dataset D , a distance metric d ( · , · ) and a failure probability δ ∈ [ 0 , 1 ] , construct a data structure which , given a query point y , reports the point x ∈ D with the smallest distance d ( x , y ) with probability greater than 1− δ . 3.1 Group Testing . Suppose we are given a set D of N items , k of which are positive ( “ hits ” ) and N − k of which are negative ( “ misses ” ) . The group testing problem is to identify the hits by grouping items and using a small collection of group tests . A group test is positive if and only if any item from the group is positive . The objective of group testing is to reliably identify the positive items using fewer than N group tests . The problem is noisy if the tests make i.i.d . mistakes with some false positive and false negative rate . The group testing problem may also be adaptive , where we are allowed to design test n based on the results of tests { 1 , 2 , ... n− 1 } , or non-adaptive , where we must perform all tests at once . Since the problem ’ s introduction in 1943 , there has been considerable work toward the construction of test designs under various constraints . For a recent review , see [ 1 ] . In this paper we develop near neighbor search algorithms using the noisy group testing framework . For simplicity , we mainly consider the doubly regular design , where we evenly distribute items among B tests , and we independently repeat this process R times to obtain a B×R grid of group tests ( Figure 1 ) . This block testing design is similar to that of RAMBO [ 8 ] , but with different filtering and inference algorithms . However , our algorithmic framework is compatible with any non-adaptive testing design .

Summary: Near Neighbor Search (NNS) is one of the most fundamental problems in machine learning. This paper proposes an efficient NNS method for high-dimensional data by transforming the original problem into a group testing problem. The authors provide a detailed theoretical analysis of the distance-sensitive Bloom Filters for group testing to demonstrate how the query time and the quality are bounded with proper parameters settings. Experiments validate the performance of FLINNG.