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OpenMP Joint Nonnegative Matrix Factorization

Install Instructions

This program depends on:

  • Armadillo library for linear algebra operations.
  • OpenBLAS for optimized kernels. If building with OpenBLAS and MKL is discoverable by cmake, use -DCMAKE_IGNORE_MKL=1 while building.

Once you have installed these libraries set the following environment variables.

export ARMADILLO_INCLUDE_DIR=/home/rnu/libraries/armadillo-6.600.5/include/
export LIB=$LIB:/home/rnu/libraries/openblas/lib:
export INCLUDE=$INCLUDE:/home/rnu/libraries/openblas/include:$ARMADILLO_INCLUDE_DIR:
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/rnu/libraries/openblas/lib/:
export CPATH=CPATH:$INCLUDE:
  • Create a build directory.
  • Change to the build directory
  • In case of MKL, source the $MKLROOT/bin/mkl_vars.sh intel64
  • run cmake [PATH TO THE CMakeList.txt]
  • make

Refer to the build scripts found in the build directory for a sample script.

Input types

JointNMF requires two input matrices, the features and connections matrices, to run on. PLANC is able to support any sparsity combination of the inputs to run on be default. However, specialized builds can be constructed for each type if needed via the following cmake flags.

  1. -DCMAKE_BUILD_SPSP=1 sets the build to expect sparse features and sparse connections matrices.
  2. -DCMAKE_BUILD_SPDEN=1 sets the build to expect sparse features and dense connections matrices.
  3. -DCMAKE_BUILD_DENSP=1 sets the build to expect dense features and sparse connections matrices.
  4. -DCMAKE_BUILD_DENDEN=1 sets the build to expect dense features and dense connections matrices.

Intel MKL vs OpenBLAS

  • export LD_LIBRARY_PATH=MKL_LIB path
  • source the $MKLROOT/bin/mkl_vars.sh intel64

Runtime usage

Tell OpenBLAS how many threads you want to use. For example on a quad core system use the following.

export OPENBLAS_NUM_THREADS=4
export MKL_NUM_THREADS=4
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:MKL_LIB

Command Line options

The specialized command line options for the JointNMF binaries are given below.

  • --mat_type: An array parameter specifying the sparsity type of the input matrices.
  • --alpha: This parameter modifies the objective function to control the importance given to each of the fit terms (features and connections).
  • --beta: The symmetric regularization parameter in the ANLS algorithm.
  • --gamma: The momentum term for the PGD algorithm.

Citation

Please refer to the papers section for the appropriate reports to cite.