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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 Technical report: I. Venetis, A. Kouris, A. Sobczyk, E. Gallopoulos and A.H. Sameh, "A direct tridiagonal solver based on Givens rotations for GPU-based architectures", HPCLAB-SCG-06/11-14, CEID, University of Patras, Nov. 2014, Available upon request. The report is under revision - we expect final release in March. |
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