CODEWITHSUNDEEP
nearest-neighborlocality-sensitive-hash
jonty rhodes
jonty rhodes

Reputation: 91

Nearest Neighbour - Locality Sensitive Hashing Disadvantage

Locality sensitive hashing seems like a great technique for KNNs without any disadvantages. However, what would be a disadvantage of locality sensitive hashing if someone is using it in industry for practical applications? Under what situations will the LSH fail or do somewhat badly? Or does it take long time to code/tune?

Upvotes: 4

Views: 1202

Answers (1)

gsamaras
gsamaras

Reputation: 73366

This is a rather broad question, but since you are new here, I will attempt to answer.

LSH is not as perfect as you describe, of course, search for papers about it please. Maybe that question can help: How to understand Locality Sensitive Hashing?

There are many LSH libraries that provides automatic parameter configuration, but not for the most important one, R, used in solving a randomized version of R-near neighbor. This is a major drawback, since the user has to manually identify R at every input. That in my opinion, is a very important aspect you have to take into account, when it comes to practical applications.

About the performance, it all depends on your input! For, example in the kd-GeRaF project of mine, I had tested LSH thoroughly and I had seen that it may have some important issues when it comes to accuracy and search speed. The scope of the datasets where in a high dimensional space, where ANNS was performed.

Upvotes: 3

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