How do I traverse a KDTree to find k nearest neighbors?
Solution 1
You can maintain a max heap of size k (k is the count of nearest neighbors which we wanted to find).
Start from the root node and insert the distance value in the max heap node. Keep on searching in k-d tree using dimensional splitting , criteria and keep updating Max Heap tree.
~Ashish
Solution 2
Adding to @Ashish's answer, you can use a max-heap in the following manner:
1) Build a max-heap of the first k elements (arr[0] to arr[k-1]) of the given array.
This step is O(k). Then
2) For each element, after the kth element (arr[k] to arr[n-1]), compare it with
root of the max-heap.
a) If the element is smaller than the root then make it root
and call heapify for max-heap.
b) Else ignore it.
The step 2 is O((n-k)*log(k)).
3) Finally, the max-heap has k smallest elements and root of the heap
is the kth smallest element.
Time Complexity: O(k + (n-k)*log(k)) without sorted output. If sorted output is needed then O(k + (n-k)*log(k) + k*log(k)).
user2647513
Updated on June 08, 2022Comments
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user2647513 almost 2 years
This question concerns the implementation of KNN searching of KDTrees. Traversal of a KDTree to find a single best match (nearest neighbor) is straightforward, akin to a modified binary search.
How is the traversal modified to exhaustively and efficiently find k-best matches (KNN)?
Edit for clarification: After finding the nearest node M to the input query I, how does the traversal algorithm continue to find the remaining K-1 closest matches to the query? Is there a traversal pattern which guarantees that nodes are visited in order of best to worst match to the query?