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https://issues.apache.org/jira/browse/MAHOUT-24?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12587939#action_12587939
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Samee Zahur commented on MAHOUT-24:
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I think I am now a bit confused about the nips paper. Correct me if I am wrong
here, but it does seem like the paper describes a system where there is only
one input variable x and only one output y. There, x[i] seems to represent
various input values and y[i] the observed output at those values. If that is
the case, my code above works perfectly. There, I have provided just one input
variable and multiple output variable for each point.
If, however, we want the number of input variables to increase, I'll need to
invert a symmetric d x d matrix where d is the number of input variables (or
input vector dimension). I could use any algorithm at all, but most seem to use
O(d^3) time which scale pretty badly. Any ideas?
> Skeletal LWLR implementation
> ----------------------------
>
> Key: MAHOUT-24
> URL: https://issues.apache.org/jira/browse/MAHOUT-24
> Project: Mahout
> Issue Type: New Feature
> Environment: n/a
> Reporter: Samee Zahur
> Attachments: LWLR.patch.tar.bz2
>
>
> This is a very skeletal but functional implementation for LWLR. It outputs n
> lines where n is the number of dimensions. ith line = sum(x[i]*x[ind]) where
> ind is the index of independant variable. So the actual gradient = 2nd
> line/1st line for the classical 2D.
> Contains a single small test case for demonstration.
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