Hi guys, I am new to ML and was experimenting with kNN search algorithms.

I have very high dimensional data 1000+ dimensions. What data structure is best suited for such high dimensional data.

I can bring down the dimensions to apprix 150 using using PCA without being too lossy.

Even then I am having hard time finding techniques that work with such high dimensional data. I am not looking for Approximate NN search using LSH.

What is the best technique that can be used here, kd tree doesn’t work well with high dimensional data, would Rtree or ball tree be a better choice or something different?

  • eamonnkeogh@alien.topBEnglish
    1·
    1 year ago

    Have you tried to simply use highly optimized brute force search?

    You can optimize with early abandoning and reordering etc.

    It is surprising how competitive that can be,