Multi-PQTable for Approximate Nearest-Neighbor Search

Nearest neighbor search is a fundamental problem in computer vision and machine learning. The straightforward solution, linear scan, is both computationally and memory intensive in large scale high-dimensional cases, hence is not preferable in practice. Therefore, there have been a lot of interests in algorithms that perform approximate nearest neighbor (ANN) search. In this paper, we propose a novel addition-based vector quantization algorithm, Asymmetric Mapping Quantization (AMQ), to efficiently conduct ANN search. Unlike existing addition-based quantization methods that suffer from handling the problem caused by the norm of database vector, we map the query vector and database vector using different mapping functions to transform the computation of L-2 distance to inner product similarity, thus do not need to evaluate the norm of database vector. Moreover, we further propose Distributed Asymmetric Mapping Quantization (DAMQ) to enable AMQ to work on very large dataset by distributed learning. Extensive experiments on approximate nearest neighbor search and image retrieval validate the merits of the proposed AMQ and DAMQ.

Exploiting lower bounds to accelerate approximate nearest neighbor search on high-dimensional data

Private approximate nearest neighbor search for on-chain data based on locality-sensitive hashing

We present a novel algorithm of exemplar-based image inpainting which can achieve an interactive response and generate results with good quality. In this paper, we modify exemplar-based method with the use of Discrete Cosine Transformation (DCT) for the strategy of exemplar matching. We decompose exemplars by DCT and evaluate the matching score with fewer coefficients, which is unprecedented in image inpainting. The reason why using fewer coefficients is so important is that the efficiency of Approximate Nearest Neighbor (ANN) search drops significantly when using high dimensions. We have also developed a local gradient-based filling algorithm to complete the image blocks with unknown pixels so that the ANN search can be adopted to speed up the matching while preserving the continuity of image. Experimental results prove the advantage of this proposed method.

Approximate nearest neighbor (ANN) search is a fundamental search in multi-dimensional databases, which has numerous real-world applications, such as image retrieval, recommendation, entity resolution, and sequence matching. Proximity graph (PG) has been the state-of-the-art index for ANN search. However, the search on existing PGs either suffers from a high time complexity or has no performance guarantee on the search result. In this paper, we propose a novel τ-monotonic graph (τ- MG) to address the limitations. The novelty of τ-MG lies in a τ-monotonic property. Based on this property, we prove that if the distance between a query q and its nearest neighbor is less than a constant τ, the search on τ-MG guarantees to find the exact nearest neighbor of q and the time complexity of the search is smaller than all existing PG-based methods. For index construction efficiency, we propose an approximate variant of τ-MG, namely τ-monotonic neighborhood graph (τ- MNG), which only requires the neighborhood of each node to be τ-monotonic. We further propose an optimization to reduce the number of distance computations in search. Our extensive experiments show that our techniques outperform all existing methods on well-known real-world datasets.

Nearest neighbor search algorithms have been successful in finding practically useful solutions to computationally difficult problems. In the nearest neighbor search problem, the brute force approach is often more efficient than other algorithms for high-dimensional spaces. A special case exists for objects represented as sparse vectors, where algorithms take advantage of the fact that an object has a zero value for most features. In general, since exact nearest neighbor search methods suffer from the “curse of dimensionality,” many practitioners use approximate nearest neighbor search algorithms when faced with high dimensionality or large datasets. To a reasonable degree, it is known that relying on approximate nearest neighbors leads to some error in the solutions to the underlying data mining problems the neighbors are used to solve. However, no one has attempted to quantify this error or provide practitioners with guidance in choosing appropriate search methods for their task. In this thesis, we conduct several experiments on recommender systems with a goal to find the degree to which approximate nearest neighbor algorithms are subject to these types of error propagation problems. Additionally, we provide persuasive evidence on the trade-off between search performance and analytics effectiveness. Our experimental evaluation demonstrates that a state-of-the-art approximate nearest neighbor search method (L2KNNGApprox) is not an effective solution in most cases. When tuned to achieve high search recall (80% or higher), it provides a fairly competitive recommendation performance compared to an efficient exact search method but offers no advantage in terms of efficiency (0.1x—1.5x speedup). Low search recall (<60%) leads to poor recommendation performance. Finally, medium recall values (60%—80%) lead to reasonable recommendation performance but are hard to achieve and offer only a modest gain in efficiency (1.5x—2.3x).

Proximity graphs (PG) have gained increasing popularity as the state-of-the-art solutions to k -approximate nearest neighbor ( k -ANN) search on high-dimensional data, which serves as a fundamental function in various fields, e.g., retrieval-augmented generation. Although PG-based approaches have the best k -ANN search performance, their index construction cost is superlinear to the number of points. Such superlinear cost substantially limits their scalability in the era of big data. Hence, the goal of this paper is to accelerate the construction of PG-based methods without compromising their k -ANN search performance. To achieve this goal, two mainstream categories of PG are revisited: relative neighborhood graph (RNG) and navigable small world graph (NSWG). By revisiting their construction process, we find the issues of construction efficiency. To address these issues, we propose a new construction framework with a novel pruning strategy for edge selection, which accelerates RNG construction while keeping its k -ANN search performance. Then, we integrate this framework into NSWG construction to enhance both the construction efficiency and k -ANN search performance of NSWG. Extensive experiments are conducted to validate our construction framework for both RNG and NSWG, and that it significantly reduces the PG construction cost, achieving up to 5.6x speedup, while not compromising the k -ANN search performance.

Recently, Approximate Nearest Neighbor (ANN) Search has become a very popular approach for similarity search on large-scale datasets. In this paper, we propose a novel vector quantization method for ANN, which introduces a joint multi-layer K-Means clustering solution for determination of the codebooks. The performance of the proposed method is improved further by a joint encoding scheme. Experimental results verify the success of the proposed algorithm as it outperforms the state-of-the-art methods.

Hashing, a useful solution for approximate nearest neighbor (ANN) search, is popular for large-scale image retrieval. In this paper, we presents a deep supervised hashing model for remote sensing image retrieval (RSIR) in the framework of generative adversarial networks (GAN), named GAN-assist Hashing (GAAH). First, to learn the compact and useful hash codes from the images, we define a novel loss function for the generator. The loss function mainly consists of classification, similarity, and bits entropy terms. The classification term makes the hash code is discriminative, the similarity term constrains the binary code is similarity preserving, and the bits entropy term assures the learned code is low-error in the quantization. Second, we construct the unique true matrix with the uniform distribution as the input of discriminator to limit the leaned hash codes are bit balanced. The final hash code is learned by a minimax optimization. The positive experimental results on a ground-truth remote sensing image archive validate the usefulness of our GAAH model. Compare with the popular deep hashing methods, our GAAH achieves improved performance.

Fast retrieval in large-scale database with high-dimensional subspaces is an important task in many applications, such as image retrieval, video retrieval and visual recognition. This can be facilitated by approximate nearest subspace (ANS) retrieval which requires effective subspace representation. Most of the existing methods for this problem represent subspace by point in the Euclidean space or the Grassmannian space before applying the approximate nearest neighbor (ANN) search. However, the efficiency of these methods can not be guaranteed because the subspace representation step can be very time consuming when coping with high dimensional data. Moreover, the transforming process for subspace to point will cause subspace structural information loss which influence the retrieval accuracy. In this paper, we present a new approach for hashing-based ANS retrieval. The proposed method learns the binary codes for given subspace set following a similarity preserving criterion. It simultaneously leverages the learned binary codes to train matrix classifiers as hash functions. This method can directly binarize a subspace without transforming it into a vector. Therefore, it can efficiently solve the large-scale and high-dimensional multimedia data retrieval problem. Experiments on face recognition and video retrieval show that our method outperforms several state-of-the-art methods in both efficiency and accuracy.

This paper addresses the problem of Approximate Nearest Neighbor (ANN) search in pattern recognition where feature vectors in a database are encoded as compact codes in order to speed-up the similarity search in large-scale databases. Considering the ANN problem from an information-theoretic perspective, we interpret it as an encoding, which maps the original feature vectors to a less entropic sparse representation while requiring them to be as informative as possible. We then define the coding gain for ANN search using information-theoretic measures. We next show that the classical approach to this problem, which consists of binarization of the projected vectors is sub-optimal. Instead, a properly designed ternary encoding achieves higher coding gains and lower complexity.

Research Context: Nearest Neighbor Search (NNS) is a fundamental problem in computing, with applications in recommendation systems, semantic search, information retrieval, and Retrieval-Augmented Generation (RAG) in Large Language Models (LLMs). High-dimensional datasets with millions of vectors make exact search computationally intractable, demanding efficient approximations. Practical Problem: Approximate Nearest Neighbor Search (ANNS) has become the standard approach, with graph-based methods standing out. However, structures such as Hierarchical Navigable Small World (HNSW) graphs impose high construction and update costs, which limits their scalability in real-world scenarios. Proposed Solution: This paper proposes the integration of Canopy Clustering as a divide-and-conquer heuristic to mitigate the computational burden of HNSW graph construction under cosine similarity, enabling more cost-effective ANNS pipelines in information retrieval systems. Related IS Theory: The research aligns with Information Systems theories on efficiency of knowledge retrieval, and the optimization of data-intensive processes in decision-support environments. It builds upon principles of data organization and access structures in IS, particularly the trade-offs between accuracy and computational feasibility in large-scale information retrieval. Research Method: Controlled experiments were conducted to evaluate the applicability of divide-and-conquer and Canopy Clustering prior to HNSW construction. Comparative analyses measured build time, query latency, and recall performance. Summary of Results: Preliminary findings suggest that Canopy Clustering can reduce construction costs while sustaining recall levels adequate for IS applications. Contributions and Impact to IS area: The work introduces a novel heuristic combination to enhance the scalability of graph-based similarity search indexing, providing methodological advances that may extend to other high-dimensional data processing tasks. It offers actionable insights for designing more efficient and viable intelligent information systems.

Hierarchical quantization for billion-scale similarity retrieval on GPUs

Approximate nearest neighbor search (ANNS) in high-dimensional vector spaces has become increasingly crucial in database and machine learning applications. Most previous ANNS algorithms require TB-scale memory to store indices of billion-scale datasets, making their deployment extremely expensive for high-performance ANNS services. The emerging SmartSSD technology offers an opportunity to achieve scalable ANNS via near data processing (NDP). However, there remain several challenges to directly adopt existing ANNS algorithms on multiple SmartSSDs. In this paper, we present SmartANNS, a SmartSSD-empowered billion-scale ANNS solution based on a hierarchical indexing methodology. We propose several novel designs to achieve near-linear scaling with multiple SmartSSDs. First, we propose a “host CPUs + SmartSSDs” cooperative architecture incorporated with hierarchical indices to significantly reduce data accesses and computations on SmartSSDs. Second, we propose dynamic task scheduling based on optimized data layout to achieve both load balancing and data reusing for multiple SmartSSDs. Third, we further propose a learning-based shard pruning algorithm to eliminate unnecessary computations on SmartSSDs. We implement SmartANNS using Samsung’s commercial SmartSSDs. Experimental results show that SmartANNS can improve query per second (QPS) by up to 10.7 × compared with the state-of-the-art SmartSSD-based ANNS solution–CSDANNS. Moreover, SmartANNS can achieve near-linear performance scalability for large-scale datasets using multiple SmartSSDs.

Approximate Nearest Neighbor Search (ANNS) is a critical problem in vector databases. Cluster-based index is utilized to narrow the search scope of ANNS, thereby accelerating the search process. Due to its scalability, it is widely employed in real-world vector search systems. However, existing cluster-based indexes often suffer from coarse granularity, requiring query vectors to compute distances with vectors of varying quality, thus increasing query complexity. Existing work aim to represent vectors with minimal cost, such as using product quantization (PQ) or linear transformations, to speed up ANNS. However, these approaches do not address the coarse granularity inherent in cluster-based index. In this paper, we present an efficient vector data query engine to enhance the granularity of cluster-based index by carefully subdividing clusters using diverse distance metrics. Building on this refined index, we introduce techniques that leverage triangle inequalities to develop highly optimized and distinct search strategies for clusters and vectors of varying qualities, thereby reducing the overhead of ANNS. Extensive experiments demonstrate that our method significantly outperforms existing in-memory cluster-based indexing algorithms, achieving up to an impressive 10× speedup and a pruning ratio exceeding 99.4%.