LSA - fails when reducing dimentsions

[jorgtied]

I get the following error message when using LSAMain even though my command line arguments are correct (-n 100). It works with a smaller portion of the same corpus. What could be wrong?

….
Info: Scaling the entropy of the rows
jan 21, 2014 8:51:52 FM edu.ucla.sspace.lsa.LatentSemanticAnalysis processSpace
Info: reducing to 100 dimensions
Exception in thread "main" java.lang.IllegalArgumentException: dimensions must be positive
at edu.ucla.sspace.matrix.OnDiskMatrix.(OnDiskMatrix.java:98)
at edu.ucla.sspace.matrix.Matrices.create(Matrices.java:216)
at edu.ucla.sspace.matrix.MatrixIO.readDenseTextMatrix(MatrixIO.java:927)
at edu.ucla.sspace.matrix.MatrixIO.readMatrix(MatrixIO.java:795)
at edu.ucla.sspace.matrix.MatrixIO.readMatrix(MatrixIO.java:762)
at edu.ucla.sspace.matrix.factorization.SingularValueDecompositionOctave.factorize(SingularValueDecompositionOctave.java:137)
at edu.ucla.sspace.lsa.LatentSemanticAnalysis.processSpace(LatentSemanticAnalysis.java:439)
at edu.ucla.sspace.mains.GenericMain.processDocumentsAndSpace(GenericMain.java:514)
at edu.ucla.sspace.mains.GenericMain.run(GenericMain.java:443)
at edu.ucla.sspace.mains.LSAMain.main(LSAMain.java:167)

[N2D2]

How big is the corpus (documents and terms)? The temporary generated Matlab Matrices of U, V and S are generated sucscessfully? The Term-Doc-Matrix (matlab-sparse-matrix6106521009675059906.dat i. e.) is generated successfully? You use the same configuration for the smaller corpus that works?

Take notice of #26

[jorgtied]

I switched to SVDLIBC and now it works fine. I'm not sure what kind of library was used as default. Maybe there is a bug in that software?