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From Apache Wiki <wikidi...@apache.org>
Subject [Hadoop Wiki] Trivial Update of "Matrix" by udanax
Date Tue, 29 Jan 2008 06:25:20 GMT
Dear Wiki user,

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The following page has been changed by udanax:
http://wiki.apache.org/hadoop/Matrix

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  == Hbase Matrix Package for Map/Reduce-based Parallel Matrix Computations ==
  
- The matrix package will be useful for some of the large-scale numeric analysis and data
mining which need the computation system of the Inverse Matrix for Data Mining related area
(e.g. linear regression, PCA, SVM, ..., etc).
+ The matrix package will be useful for some of the Large-Scale Numeric Analysis and Data
Mining which need the computation system of the Inverse Matrix for Data Mining related area
(e.g. linear regression, PCA, SVM, ..., etc).
  
- The current shared-memory based parallel matrix solution provides a scalable and high performance
matrix operations, however, matrix resources can't be scalable. But, Using Hbase's Row,Column(Qualifier)
two dimensional space, we are able to store large sparse matrix. Also, The Auto-partitioned
sparsity sub-structure will be efficiently managed and serviced by Hbase. Row or Column operations
can be done in linear time and algorithms such as structured Gaussian elimination or iterative
methods run in O(~-the number of non-zero elements in the matrix/number of mappers (processors/cores)-~)
time on Map/Reduce.
+ The current shared-memory based parallel matrix solution provides a scalable and high performance
matrix operations, however, matrix resources can't be scalable. But, Using Hbase's Row,Column(Qualifier)
two dimensional space, we are able to store large sparse matrix. Also, The Auto-partitioned
sparsity sub-structure will be efficiently managed and serviced by Hbase. Row or Column operations
can be done in linear time and algorithms such as structured Gaussian elimination or iterative
methods run in O(~-the number of non-zero elements in the matrix-~ / ~-number of mappers (processors/cores)-~)
time on Map/Reduce.
  
  === Initial Contributors ===
  

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