Searching in one billion vectors: re-rank with source coding (1102.3828v1)

Abstract: Recent indexing techniques inspired by source coding have been shown successful to index billions of high-dimensional vectors in memory. In this paper, we propose an approach that re-ranks the neighbor hypotheses obtained by these compressed-domain indexing methods. In contrast to the usual post-verification scheme, which performs exact distance calculation on the short-list of hypotheses, the estimated distances are refined based on short quantization codes, to avoid reading the full vectors from disk. We have released a new public dataset of one billion 128-dimensional vectors and proposed an experimental setup to evaluate high dimensional indexing algorithms on a realistic scale. Experiments show that our method accurately and efficiently re-ranks the neighbor hypotheses using little memory compared to the full vectors representation.

• The paper presents an innovative re-ranking method that leverages source coding to refine quantized distance estimates without accessing full vectors.
• The paper introduces a benchmark dataset of one billion 128-dimensional vectors to rigorously test indexing efficiency under realistic, large-scale conditions.
• Extensive experiments demonstrate that the proposed method enhances search precision while reducing memory and disk I/O compared to traditional approaches.

Searching in One Billion Vectors: Re-rank with Source Coding

The paper under discussion presents a novel approach to the problem of efficient searching within large-scale vector datasets, specifically targeting scenarios involving one billion vectors of high dimensionality. The primary contribution lies in an adapted post-verification scheme that employs source coding techniques, circumventing the need for exhaustive distance computations on full vector representations.

The core technique exploits quantization mechanisms from source coding to facilitate the re-ranking of neighbor hypotheses generated by compressed-domain indexing methods. Departing from traditional approaches that rely on exact Euclidean distance calculations during post-verification, this method proposes the refinement of estimated distances using short quantization codes. This adjustment effectively reduces the necessity of reading the full vectors from external storage, thus achieving significant memory and computational efficiency.

Key Contributions

1. Memory-efficient Re-ranking: The proposed methodology leverages existing source coding adaptations to enhance indexing efficiency. By refining rough distance estimates with concise quantization codes, the approach obviates the need for accessing full vector data from disks, which is particularly advantageous in scenarios dealing with exorbitant data volumes.
2. Dataset Introduction: The authors introduce a substantial public dataset comprising one billion 128-dimensional vectors, structured to simulate realistic scales typical in high-dimensional indexing challenges. This dataset facilitates performance evaluations for different algorithms and represents the largest known benchmark of its kind evaluated against exact linear scans.
3. Experimental Validation: Extensive experimentation underscores the proposed method's capability to enhance search precision significantly. The refined re-ranking leads to robust performance parity with state-of-the-art alternatives while maintaining reduced memory footprints.

Technical Insights

The indexing and search methodology capitalizes on the Asymmetric Distance Computation (ADC) introduced previously by Jegou et al. Product quantizers calculate compressed encodings of vectors and enable approximate nearest neighbor searches that scale imperceptibly with the growth of dataset size. This paper extends these principles by introducing refinements to vector approximations through a product quantizer that mitigates the quantization error further. Each database vector is segmented into subvectors that undergo independent quantization, allowing unprecedented scalability and search efficiency.

Further, the authors propose an IVFADC variant which integrates an inverted file structure to further streamline processing by reducing the number of vectors examined during the candidate selection phase. This adjustment is pivotal in handling voluminous vector sets within practical time constraints.

Implications and Future Directions

The technique indicative of the research carries both practical and theoretical repercussions in the management of enormous datasets typical in domains such as computer vision and multimedia retrieval. The balance struck between memory conservation and search accuracy heralds potential for broad application in environments constrained by memory but demanding high throughput and accuracy.

In future research, the refinements introduced could potentially be adapted to other high-dimensional indexing schemas, extending their utility. Furthermore, considering the ongoing evolution in AI hardware accelerations, such as GPUs and TPUs, the integration of these methods to leverage parallel processing could yield additional performance gains, opening novel avenues for optimization in vector search tasks.

In conclusion, the paper presents an advanced approach to vector search problems, leveraging source coding to tackle long-standing challenges in memory and efficiency. The results delineated assure significant progression in the context of large-scale data retrieval systems, evidencing the pertinence of intelligent indexing and re-ranking strategies in modern data science.