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Is there an online filter algorithm that generates an optimal set (maximizes correlation of prediction) of feature weights (ie relative feature scaling) for a simple 1NN (or KNN) regression using either Manhattan or Euclidean distance metrics? I have a set of numerical X and y values, and I'd like to create a simple nearest neighbor predictor, but I'm unsure how to optimize the feature weighting. I tried r-relieff, but it seems like brute force forward feature selection works better (but is too slow). Is there a way I can reformulate into a qp problem when both X and y are all numerical and can be solved efficiently using an online scheme (ie single pass similar to r-relieff)?

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