Approximate Nearest Neighbor Search(ANNS)目录Approximate Nearest Neighbor Search(ANNS) 树方法 kd-tree在一个给定的空间(或集合)中找到距离兴趣(或目标)对象最近的邻居,这个问题在多种领域都是非常基本而重要的, 如生物、医学、地理、机器人、互联网等等只要涉及数据检索、机器学习、大规模数据处理等基本都会需要。
ANNS(Approximate Nearest Neighbor Search)在非结构化数据的处理中扮演着重要的角色,无论是推荐系统还是数据分析,都搭建在ANNS的底层能力之上。 而我们所熟知的IVF、PQ、HNSW等算法都属于in-memory ANNS算法,也就是说,全量数据都存储在内存之中等待query请求的到来,完成一次检索操作。尽管我们也提过,GPU、NPU等异构计...
Spotlight presentation, Microsoft News: https://news.microsoft.com/source/features/innovation/bing-vector-search/ PDF 下载BibTex The in-memory algorithms for approximate nearest neighbor search (ANNS) have achieved great success for fast high-recall search, but are extremely ex...
Query-directed searchHigh-dimensional approximate nearest neighbor search (ANNS) has drawn much attention over decades due to its importance in machine learning and massive data processing. Recently, the graph-based ANNS become more and more popular thanks to the outstanding search performance. While ...
作者的初衷是把不同领域(比如机器学习ML,多媒体MM,数据库DB,工业界)的能解决同一个问题(近似NN,ANNS)的方法拿出来,进行比较。 ANNS算法可以由多个层面去衡量: 搜索时间复杂度; 搜索质量; 索引大小; 可扩展性(数据量、维度); 鲁棒性; 可更新性;
Approximate Nearest neighbor search (ANNS) is fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision. Although many algorithms have been continuously proposed in the literature in the above domains each year, there is ...
Approximate nearest neighbor search (ANNS) is a fundamental problem in databases and data mining. A scalable ANNS algorithm should be both memory-efficient and fast. Some early graph-based approaches have shown attractive theoretical guarantees on search time complexity, but they all suffer from the...
对于ANNS问题,扩展性到达十亿级别的分两类: 乘积量化+翻转索引的:这类的问题在于乘积量化作为一种有损压缩,会导致在大数据集的情况下,精度下降严重。 乘积量化:https://www.jianshu.com/p/533ac746748b 优化乘积量化:https://www.jianshu.com/p/ecf37bbca6c1 ...
AnnLiteis alightweightandembeddablelibrary forfastandfilterableapproximate nearest neighbor search(ANNS). It allows to search for nearest neighbors in a dataset of millions of points with a Pythonic API. Highlighted features: 🐥Easy-to-use: a simple API is designed to be used with Python. It ...
NSG is a graph-based approximate nearest neighbor search (ANNS) algorithm. It provides a flexible and efficient solution for the metric-free large-scale ANNS on dense real vectors. It implements the algorithm of our PVLDB paper -Fast Approximate Nearest Neighbor Search With The Navigating Spread-...