Some links might not be active; this could be because the document has not been archived yet, or because it is under revision, or because it cannot be disclosed or because it is only available in hardcopy form. Please notify us if you need something that appears not to be available. Please note that "CEID" is an acronym for the "Computer Engineering and Informatics Department" and "TR" for "Technical Report". Some reports are available from http://arxiv.org. Finally, please note the copyright restrictions on some of these publications that might limit such downloads to users from non-profit and educational institutions.
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. Primary contact: Dimitrios Zeimpekis Funding: University of Patras K. Karatheodori grant no. B120; Bodossaki Foundation Graduate Fellowship. Links: TMG wiki Some uses of TMG |
| 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. Primary contact: Georgios Kollias Funding: PYTHAGORAS I grant, project B365016; GRID-APP project of Hellenic General Secreteriat of Research and Technology Links: Jylab wiki |
| 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. Primary contact: Christos Boutsidis Funding: University of Patras K. Karatheodori grant no. B120 Links: NNDSVD presentation, major publication |
| 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. Primary contact: Effrosyni Kokiopoulou Funding: University of Patras K. Karatheodori grant no. B120; Bodossaki Foundation Graduate Fellowship. Links: TBA, major publication |
Publications