Fast k-NN search

نویسندگان

  • Ville Hyvönen
  • Teemu Pitkänen
  • Sotiris K. Tasoulis
  • Liang Wang
  • Teemu Roos
  • Jukka Corander
چکیده

Random projection trees have proven to be effective for approximate nearest neighbor searches in high dimensional spaces where conventional methods are not applicable due to excessive usage of memory and computational time. We show that building multiple trees on the same data can improve the performance even further, without significantly increasing the total computational cost of queries when executed in a modern parallel computing environment. Our experiments identify suitable parameter values to achieve accurate searches with extremely fast query times, while also retaining a feasible complexity for index construction.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Extending LAESA Fast Nearest Neighbour Algorithm to Find the k Nearest Neighbours

Many pattern recognition tasks make use of the k nearest neighbour (k–NN) technique. In this paper we are interested on fast k– NN search algorithms that can work in any metric space i.e. they are not restricted to Euclidean–like distance functions. Only symmetric and triangle inequality properties are required for the distance. A large set of such fast k–NN search algorithms have been develope...

متن کامل

Some improvements on NN based classifiers in metric spaces

The nearest neighbour (NN) and k-nearest neighbour (k-NN) classification rules have been widely used in Pattern Recognition due to its simplicity and good behaviour. Exhaustive nearest neighbour search may become unpractical when facing large training sets, high dimensional data or expensive dissimilarity measures (distances). During the last years a lot of fast NN search algorithms have been d...

متن کامل

Extending Fast Nearest Neighbour Search Algorithms for Approximate k-NN Classification

The nearest neighbour (NN) and k-nearest neighbour (kNN) classi cation rules have been widely used in pattern recognition due to its simplicity and good behaviour. Exhaustive nearest neighbour search can become unpractical when facing large training sets, high dimensional data or expensive similarity measures. In the last years a lot of NN search algorithms have been developed to overcome those...

متن کامل

A fast algorithm for finding k-nearest neighbors with non-metric dissimilarity

Fast nearest neighbor (NN ) finding has been extensively studied. While some fast NN algorithms using metrics rely on the essential properties of metric spaces, the others using non-metric measures fail for large-size templates. However, in some applications with very large size templates, the best performance is achieved by NN methods based on the dissimilarity measures resulting in a special ...

متن کامل

Fast Online k-nn Graph Building

In this paper we propose an online approximate k-nn graph building algorithm, which is able to quickly update a k-nn graph using a flow of data points. One very important step of the algorithm consists in using the current distributed graph to search for the neighbors of a new node. Hence we also propose a distributed partitioning method based on balanced k-medoids clustering, that we use to op...

متن کامل

Approximate Nearest Neighbour Search with the Fukunaga and Narendra Algorithm and Its Application to Chromosome Classification

The nearest neighbour (NN) rule is widely used in pattern recognition tasks due to its simplicity and its good behaviour. Many fast NN search algorithms have been developed during last years. However, in some classification tasks an exact NN search is too slow, and a way to quicken the search is required. To face these tasks it is possible to use approximate NN search, which usually increases e...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • CoRR

دوره abs/1509.06957  شماره 

صفحات  -

تاریخ انتشار 2015