Probably correct k-nearest neighbor search in high dimensions
نویسندگان
چکیده
منابع مشابه
Probably correct k-nearest neighbor search in high dimensions
A novel approach for k-nearest neighbor (k-NN) searching with Euclidean metric is described. It is well known that many sophisticated algorithms cannot beat the brute-force algorithm when the dimensionality is high. In this study, a probably correct approach, in which the correct set of k-nearest neighbors is obtained in high probability, is proposed for greatly reducing the searching time. We ...
متن کاملlsh, Nearest neighbor search in high dimensions
Calculating distance pairs is O(n2) in memory and time and finding the nearest neighbor is O(n) in time. Tree indexing techniques like kd-tree [2] were developed to cope with large n, however their performance quickly breaks down for p > 3 [3]. Locality sensitive hashing (LSH) [3] is a technique for generating hash numbers from high dimensional data, such that nearby points have identical hashe...
متن کاملParallel Algorithms for Nearest Neighbor Search Problems in High Dimensions
The nearest neighbor search problem in general dimensions finds application in computational geometry, computational statistics, pattern recognition, and machine learning. Although there is a significant body of work on theory and algorithms, surprisingly little work has been done on algorithms for high-end computing platforms and no open source library exists that can scale efficiently to thou...
متن کاملA Simple Algorithm for Nearest Neighbor Search in High Dimensions
Finding the closest point in a high-dimensional space is a problem that often occurs in pattern recognition. Unfortunately, the complexity of most known search algorithms grows exponentially with dimension, which makes them unsuitable for high dimensions. However, for most applications, the closest point is of interest only if it is closer than some pre-defined distance. In the article ”A Simpl...
متن کاملA Simple Algorithm for Nearest Neighbor Search in High Dimensions
The problem of finding the closest point in high-dimensional spaces is common in pattern recognition. Unfortunately, the complexity of most existing search algorithms, such as k-d tree and R-tree, grows exponentially with dimension, making them impractical for dimensionality above 15. In nearly all applications, the closest point is of interest only if it lies within a user-specified distance e...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2010
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2009.09.026