Approximate Nearest Neighbor Search (ANNS) in high dimensional spaces is crucial for many real-life applications (e.g., e-commerce, web, multimedia, etc.) dealing with an abundance of data. This paper proposes an end-to-end learning framework that couples the partitioning (one critical step of ANNS) and learning-to-search steps using a custom loss function. A key advantage of our proposed solution is that it does not require any expensive pre-processing of the dataset, which is one of the critical limitations of the state-of-the-art approach. We achieve the above edge by formulating a multi-objective custom loss function that does not need ground truth labels to quantify the quality of a given data-space partition, making it entirely unsupervised. We also propose an ensembling technique by adding varying input weights to the loss function to train an ensemble of models to enhance the search quality. On several standard benchmarks for ANNS, we show that our method beats the state-of-the-art space partitioning method and the ubiquitous K-means clustering method while using fewer parameters and shorter offline training times. We also show that incorporating our space-partitioning strategy into state-of-the-art ANNS techniques such as ScaNN can improve their performance significantly. Finally, we present our unsupervised partitioning approach as a promising alternative to many widely used clustering methods, such as K-means clustering and DBSCAN.
翻译:近距离近邻搜索( ANNS ) 在高维空间的近邻搜索( ANNS ) 对许多真实生活中的应用程序( 如电子商务、 网络、 多媒体等) 处理大量数据至关重要 。 本文提出一个端对端学习框架, 将分隔( ANNS 的关键步骤) 和学习搜索步骤结合使用自定义损失函数。 我们拟议解决方案的一个主要优点是, 它不需要对数据集进行任何昂贵的预处理, 这是最先进的方法的关键限制之一 。 我们通过制定多目标的自定义损耗函数实现以上边缘, 不需要用地面的自定义标签来量化特定数据- 空间分割的质量, 使其完全不受监督 。 我们还提出一种组合技术, 将不同的输入权重加到损失函数中, 训练一组模型来提高搜索质量 。 在许多 ANNS 标准基准上, 我们的方法比状态的空域分隔替代方法要强得多, 以及无端的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的自定义的离的离的自定义的自定义的自定义的自定义的离的离流的离的离的离流的离流的离流的离的离的离流的离的离流的离层的离的离的离层的离层的离层的离流的离流的离层的离,, 方法,,,,,,,, 将这种自制式的自制的自制的自制式的自制的自制的自制的自制的自制的自制的自制的自制的自制的自制的自制的自制的自制的自制的自制的自制式的自制的自制的自制的自制的自制的自制的自制式的自制的自制式的自制式的自制式的自制式制式的