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 ...