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https://issues.apache.org/jira/browse/FLINK-1934?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14985273#comment-14985273
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Daniel Blazevski commented on FLINK-1934:
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[~till.rohrmann] , I have been looking to approximate knn algorithms. I found
here in Ref. [3] that I added in the Description above. Ref. [3] is cited
several times in Ref. [1], and [3] mentions that the method of z-functions is
best for dimensions < 10, and that locality sensitive hashing (LSH) is better
for dimensions ~ 30 and above.
How about I give Ref [1] a shot, and maybe later down the road add LSH? If so,
feel free to assign me to this issue.
> Add approximative k-nearest-neighbours (kNN) algorithm to machine learning
> library
> ----------------------------------------------------------------------------------
>
> Key: FLINK-1934
> URL: https://issues.apache.org/jira/browse/FLINK-1934
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Raghav Chalapathy
> Labels: ML
>
> kNN is still a widely used algorithm for classification and regression.
> However, due to the computational costs of an exact implementation, it does
> not scale well to large amounts of data. Therefore, it is worthwhile to also
> add an approximative kNN implementation as proposed in [1,2]. Reference [3]
> is cited a few times in [1], and gives necessary background on the z-value
> approach.
> Resources:
> [1] https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf
> [2] http://www.computer.org/csdl/proceedings/wacv/2007/2794/00/27940028.pdf
> [3] http://cs.sjtu.edu.cn/~yaobin/papers/icde10_knn.pdf
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