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https://issues.apache.org/jira/browse/MAHOUT-986?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13288247#comment-13288247
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Shannon Quinn commented on MAHOUT-986:
--------------------------------------
This issue was brought to my attention by Dan Brickley; this is his dataset
from the dbpedia.org website. I believe it's a bunch of Wikipedia articles
(4.19 million, to be exact) whose similarities to one another are weighted in
one giant affinity matrix, and he was using spectral clustering to cluster the
articles. I can contact him directly for a precise definition of the problem.
> OutOfMemoryError in LanczosState by way of SpectralKMeans
> ---------------------------------------------------------
>
> Key: MAHOUT-986
> URL: https://issues.apache.org/jira/browse/MAHOUT-986
> Project: Mahout
> Issue Type: Improvement
> Components: Clustering
> Affects Versions: 0.6
> Environment: Ubuntu 11.10 (64-bit)
> Reporter: Shannon Quinn
> Assignee: Shannon Quinn
> Priority: Minor
> Fix For: 0.7
>
> Attachments: MAHOUT-986.patch
>
>
> Dan Brickley and I have been testing SpectralKMeans with a dbpedia dataset (
> http://danbri.org/2012/spectral/dbpedia/ ); effectively, a graph with
> 4,192,499 nodes. Not surprisingly, the LanczosSolver throws an
> OutOfMemoryError when it attempts to instantiate a DenseMatrix of dimensions
> 4192499-by-4192499 (~17.5 trillion double-precision floating point values).
> Here's the full stack trace:
> {quote}
> Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
> at org.apache.mahout.math.DenseMatrix.<init>(DenseMatrix.java:50)
> at
> org.apache.mahout.math.decomposer.lanczos.LanczosState.<init>(LanczosState.java:45)
> at
> org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver.run(SpectralKMeansDriver.java:146)
> at
> org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver.run(SpectralKMeansDriver.java:86)
> at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:65)
> at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:79)
> at
> org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver.main(SpectralKMeansDriver.java:53)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:616)
> at
> org.apache.hadoop.util.ProgramDriver$ProgramDescription.invoke(ProgramDriver.java:68)
> at org.apache.hadoop.util.ProgramDriver.driver(ProgramDriver.java:139)
> at org.apache.mahout.driver.MahoutDriver.main(MahoutDriver.java:188)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:616)
> at org.apache.hadoop.util.RunJar.main(RunJar.java:156)
> {quote}
> Obviously SKM needs a more sustainable and memory-efficient way of performing
> an eigen-decomposition of the graph laplacian. For those who are more
> knowledgeable in the linear systems solvers of Mahout than I, can the Lanczos
> parameters be tweaked to negate the requirement of a full DenseMatrix? Or
> should SKM move to SSVD instead?
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