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Some software from our group
TMG: Toolbox
that can be used for various tasks in text mining (TM)
specifically i) indexing,
ii) retrieval, iii) dimensionality reduction, iv)
clustering, v) classification. Most of TMG is
written in MATLAB, though a large segment of the indexing
phase is written in Perl. TMG
is especially suited for TM applications where data is
high-dimensional but extremely sparse as it uses the
sparse matrix infrastructure of MATLAB. Initially built as a
preprocessing tool for creating term-document matrices (tdm's)
from unstructured text that was reportedly used
with success by several
researchers and instructors, the new version of TMG (May'07)
offers a much wider range of tools. |
g-Spike:
A very fast and fairly robust direct parallel tridiagonal solver
based on Givens rotations and the Spike polyalgorithm for the
GPU. Primary contact: Ioannis Venetis Funding: By product of research conducted under the Research Funding Program: THALES: Reinforcement of the interdisciplinary and/or inter - institutional research and innovation, (MIS-379421, ``Expertise development for the aeroelastic analysis and the design-optimization of wind turbines''). Links: g-Spike page (if you cannot load the code please contact us) Reference: I. Venetis, A. Kouris, A. Sobczyk, E. Gallopoulos and A.H. Sameh, "A direct tridiagonal solver based on Givens rotations for GPU-based architectures", DOI 10.1016/j.parco.2015.03.008, Parallel Computing, Apr. 2015. See also HPCLAB-SCG-06/11-14, CEID, University of Patras, Nov. 2014. |
Jylab:
Portable and flexible
scientific computing environment running on all platforms
providing a recent JVM and enabling the development of
scientific applications over distributed computing
platforms. Jylab conveniently packages Jython (<http://www.jython.org/>)
for flexible Python language scripting, with a core set of
open source libraries implementing numerical linear algebra
routines (NLA) and communication models. Recently, a package
was implemented iin Jylab that enables accessing and using the
Grid infrastructure. |
NNDSVD: MATLAB
functions to initialize approximate nonnegative
matrix factorization algorithms.The basic algorithm
contains no randomization and is based on approximations of
positive sections of the partial SVD factors of the data
matrix utilizing an algebraic property of unit rank matrices.
The method is also suitable when seeking sparse factors.
The approximants furnished by NNDSVD appear to lead to much
faster error reduction compared to random initialization
though the eventual error is of similar quality. |
IRLANB: MATLAB
functions to compute the smallest singular triplets of large
sparse matrices in matrix free manner. The algorithms used are
based on Lanczos bidiagonalization, implicit restarting,
and harmonic Ritz values, deflation and refinement. The method
has been used with success in applications such as the
computation of matrix pseudospectra and clustering. |
Selected
publications / reports / presentations