[jira] [Comment Edited] (MADLIB-927) Initial implementation of k-NN

["Tianwei Shen (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MADLIB-927?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15173187#comment-15173187 ] 

Tianwei Shen edited comment on MADLIB-927 at 3/1/16 3:44 AM:
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Hi Sir,
I am Tianwei, a second-year Ph.D. student in HKUST. I am interested in this proposal and have implemented a prototype of naive k-nn in one of my projects, libvot(https://github.com/hlzz/libvot). See the source code for my implementation of k-nn here (https://github.com/hlzz/libvot/blob/master/src/vocab_tree/clustering.cpp), which support multi-thread processing using native c++11 support. This project is an implementation of vocabulary tree, which is a image retrieval algorithm widely used. I think this issue best suits my skill sets, so I would like to discuss with you for things like "where should I put it", "how should I adapt to the interface of MADlib" sort of stuffs, in greater depth. Thanks.



was (Author: tianwei37):
Hi Sir,
I am Tianwei, a second-year Ph.D. student in HKUST. I am interested in this proposal and have implemented a prototype of naive k-nn in one of my projects, libvot(https://github.com/hlzz/libvot). See the source code for my implementation of k-nn here (https://github.com/hlzz/libvot/blob/master/src/vocab_tree/clustering.cpp), which support multi-thread processing using native c++11 support. This project is an implementation of vocabulary tree, which is a image retrieval algorithm widely used. I think this issue best suits my skill sets, so I would like to discuss with you in greater depth. Thanks.


> Initial implementation of k-NN
> ------------------------------
>
>                 Key: MADLIB-927
>                 URL: https://issues.apache.org/jira/browse/MADLIB-927
>             Project: Apache MADlib
>          Issue Type: New Feature
>            Reporter: Rahul Iyer
>              Labels: gsoc2016, starter
>
> k-Nearest Neighbors is a very simple algorithm that is based on finding nearest neighbors of data points in a metric feature space according to a specified distance function. It is considered one of the canonical algorithms of data science. It is a nonparametric method, which makes it applicable to a lot of real-world problems, where the data doesn’t satisfy particular distribution assumptions. Also, it can be implemented as a lazy algorithm, which means there is no training phase where information in the data is condensed into coefficients, but there is a costly testing phase where all data is used to make predictions.
> This JIRA involves implementing the naïve approach - i.e. compute the k nearest neighbors by going through all points.



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