[
https://issues.apache.org/jira/browse/FLINK-1934?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15228242#comment-15228242
]
Daniel Blazevski commented on FLINK-1934:
-----------------------------------------
That all sounds good.
As of now I've only done simple tests to make sure that algorithm is working
correctly: e.g. put one test point much closer to a single training point and
make sure that is the nearest neighbor. Indeed, more tests and benchmarks are
needed -- for example to make sure that even it doesn't compute the exact
nearest neighbors, that the neighbors are "nearby".
> 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: Daniel Blazevski
> 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
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
