[jira] [Issue Comment Edited] (MAHOUT-792) Add new stochastic decomposition code

["Lance Norskog (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAHOUT-792?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13096570#comment-13096570 ] 

Lance Norskog edited comment on MAHOUT-792 at 9/4/11 12:28 AM:
---------------------------------------------------------------

Please run this:
{code}
 public static void main(String[] args) {
	  RandomTrinaryMatrix rtm = new RandomTrinaryMatrix(0, 30, 40, true);
	  for(int row = 0; row < 30; row++) 
		  for(int column = 0; column < 40; column++)
			  System.out.println(rtm.get(row, column));	
	  rtm = new RandomTrinaryMatrix(0, 30, 40, true);
	  for(int row = 0; row < 30; row++) 
		  for(int column = 0; column < 40; column++)
			  System.out.println(rtm.get(row, column));	
  }
{code}


      was (Author: lancenorskog):
    Please run this:
{code}
 public static void main(String[] args) {
	  RandomTrinaryMatrix rtm = new RandomTrinaryMatrix(0, 30, 40, true);
	  for(int row = 0; row < 30; row++) 
		  for(int column = 0; column < 40; column++)
			  System.out.println(rtm.get(row, column));	
	  rtm = new RandomTrinaryMatrix(0, 30, 40, true);
	  for(int row = 0; row < 30; row++) 
		  for(int column = 0; column < 40; column++)
			  System.out.println(rtm.get(row, column));	
  }
{code}
The "low-grade" mode does not emit 0. The high-grade mode does not emit negative numbers.

I'm curious: what is the math behind an even split of -1,0,1? The Achlioptas math says +1/-1 or +1/-1/0/0/0 . So, the low-grade mode is correct, just not what the comment describes :)

  
  
> Add new stochastic decomposition code
> -------------------------------------
>
>                 Key: MAHOUT-792
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-792
>             Project: Mahout
>          Issue Type: New Feature
>            Reporter: Ted Dunning
>         Attachments: MAHOUT-792.patch, MAHOUT-792.patch, sd-2.pdf
>
>
> I have figured out some simplification for our SSVD algorithms.  This eliminates the QR decomposition and makes life easier.
> I will produce a patch that contains the following:
>   - a CholeskyDecomposition implementation that does pivoting (and thus rank-revealing) or not.  This should actually be useful for solution of large out-of-core least squares problems.
>   - an in-memory SSVD implementation that should work for matrices up to about 1/3 of available memory.
>   - an out-of-core SSVD threaded implementation that should work for very large matrices.  It should take time about equal to the cost of reading the input matrix 4 times and will require working disk roughly equal to the size of the input.

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