I've searched quite a bit and haven't landed on any useful results.The problem statement is:Given a set of vectors, I wish to find its approximate k-nearest neighbors.The caveat here is that each of my dimensions resemble a different entity and hence we cannot use the same weight for each dimension while computing the distance.Thus, solutions like kd-tree don't work as is.Is there any data-structure or any alternate algorithm that I can use to find such approximate weighted k-nearest neighbors.Note: Multiplying the initial input data with their weights so as to get a uniform weight is not an option.
Weighted k nearest neighbor search
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