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Implementation of KNN based on the Spark-ML-LSH

Hash Function: h_i(x) = floor(r_i.dot(x) / bucketLength)
threshold = 2000
W = bucketLength
NHT = # of HashTables

  • The number of buckets will be (max L2 norm of input vectors) / bucketLength.
  • If input vectors are normalized, 1-10 times of pow(numRecords, -1/inputDim) would be a reasonable value
k NHT W Accuracy_train Accuracy_test T_index T_query
1 3 2 - 0.9087 54 175848
5 3 2 - 0.893 54 174651
9 3 2 - 0.8808 54 155673
1 5 2 - 0.9291 29 251302
5 5 2 - 0.9137 29 275162
9 5 2 - 0.9036 29 367008
1 7 2 - 0.9372 34 523696
5 7 2 - 0.9238 34 460986
9 7 2 - 0.9145 34 485565
1 3 5 - 0.9357 30 367245
5 3 5 - 0.9263 30 340930
9 3 5 - 0.9171 30 341963
1 5 5 - 0.9459 41 596984
5 5 5 - 0.9401 41 559091
9 5 5 - 0.93 41 561646
1 7 5 - 0.9496 22 770659
5 7 5 - 0.9465 22 787571
9 7 5 - 0.9385 22 841044
1 3 8 - 0.9419 37 439672
5 3 8 - 0.9348 37 417642
9 3 8 - 0.9253 37 422822
1 5 8 - 0.9481 24 605899
5 5 8 - 0.9438 24 609686
9 5 8 - 0.9358 24 609061
1 7 8 - 0.9511 22 780209
5 7 8 - 0.9447 22 769710
9 7 8 - 0.9409 22 769710

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