My problem is related to the approximate nearest neighbor search. I found a useful link Calculating the distance to the kth nearest neighbor for all points in the set which says that the search takes ...
An elaboration on this question, but with more constraints. The idea is the same, to find a simple, fast algorithm for k-nearest-neighbors in 2 euclidean dimensions. The bucketing grid seems to work ...
What are the best known results for a data structure offering the following operations on sets of points in 2-dimensional euclidean space: $insert(x)$ $delete(x)$ $nearest(k,x)$ (where $k$ is an ...
For a machine learning application, my group needs to calculate the Euclidean distance to the $k$th nearest neighbor in a set $X$ for each $x \in (X \cup Y) \subset \mathbb R^d$ (for $d$ between 5 and ...
I'm writing a program that receives data over a network connection. Every data point is simply a 4 dimensional vertex, lets call the dimensions X,Y,Z,W. The values of each dimension are exponentially ...
I have 3-dimensional data I want to store in a kd-tree. Additionally I have a domain-specific distance function in this space for which I have a hard time to prove the triangular inequality. Here is ...
Given a collection of thousands of points in 3D, I need to get the list of neighbours for each particle that fall inside some cutoff value (in terms of euclidean distance), and if possible, sorted ...
NOTE: The question has been restated in my answers: Assuming now that we can find the lowest sibling ancestors in $O(1)$ time, can the ANN be really performed in $O(\log n)$? Quadtrees are ...
I want to compute Euclidian distance between similar vectors in a database (SQLite). So each column in the database is a value from my vector. The first problem appears, I have a large number of ...