Sparse matrix reordering schemes for browsing hypertext.
M. Berry, B. Hendrickson, and P. Raghavan. 32, page 99-123. (1996)The mathematics of numerical analysis. 1995 AMS-SIAM summer seminar
in applied mathematics, July 17--August 11, 1995, Park City, UT,
USA. Providence.
Abstract
Many approaches for retrieving documents from electronic databases
depend on the literal matching of words in a user's query to the
keywords defining database objects. Since there is great diversity
in the words people use to describe the same object, literal- or
lexical- based methods can often retrieve irrelevant documents. Another
approach to exploit the implicit higher-order structure in the association
of terms with text objects is to compute the singular value decomposition
(SVD) of large sparse term by text-object matrices. Latent Semantic
Indexing (LSI) is a conceptual indexing method which employs the
SVD to represent terms and objects by dominant singular subspaces
so that user queries can be matched in a lower-rank semantic space.\par
This paper considers a third, intermediate approach to facilitate
the immediate detection of document (or term) clusters. We demonstrate
that both traditional sparse matrix reordering schemes (e.g., Reverse
Cuthill-McKee) and spectral-based approaches (e.g., Correspondence
Analysis or Fiedler vector-based spectral bisection) can be used
to permute original term by document (hypertext) matrices to a narrow-banded
form suitable for the detection of document (or term) clusters. Although
this approach would not exploit the higher-order semantic structure
in the database, it can be used to develop browsing tools for hypertext
and on-line information at a reduced computational cost.
%0 Conference Paper
%1 857.68036
%A Berry, Michael W.
%A Hendrickson, Bruce
%A Raghavan, Padma
%D 1996
%E et al. Renegar, James
%J Lect. Appl. Math.
%K decomposition; matrix reordering schemes singular sparse value
%P 99-123
%T Sparse matrix reordering schemes for browsing hypertext.
%V 32
%X Many approaches for retrieving documents from electronic databases
depend on the literal matching of words in a user's query to the
keywords defining database objects. Since there is great diversity
in the words people use to describe the same object, literal- or
lexical- based methods can often retrieve irrelevant documents. Another
approach to exploit the implicit higher-order structure in the association
of terms with text objects is to compute the singular value decomposition
(SVD) of large sparse term by text-object matrices. Latent Semantic
Indexing (LSI) is a conceptual indexing method which employs the
SVD to represent terms and objects by dominant singular subspaces
so that user queries can be matched in a lower-rank semantic space.\par
This paper considers a third, intermediate approach to facilitate
the immediate detection of document (or term) clusters. We demonstrate
that both traditional sparse matrix reordering schemes (e.g., Reverse
Cuthill-McKee) and spectral-based approaches (e.g., Correspondence
Analysis or Fiedler vector-based spectral bisection) can be used
to permute original term by document (hypertext) matrices to a narrow-banded
form suitable for the detection of document (or term) clusters. Although
this approach would not exploit the higher-order semantic structure
in the database, it can be used to develop browsing tools for hypertext
and on-line information at a reduced computational cost.
%@ 0-8218-0530-4/pbk
@inproceedings{857.68036,
abstract = {Many approaches for retrieving documents from electronic databases
depend on the literal matching of words in a user's query to the
keywords defining database objects. Since there is great diversity
in the words people use to describe the same object, literal- or
lexical- based methods can often retrieve irrelevant documents. Another
approach to exploit the implicit higher-order structure in the association
of terms with text objects is to compute the singular value decomposition
(SVD) of large sparse term by text-object matrices. Latent Semantic
Indexing (LSI) is a conceptual indexing method which employs the
SVD to represent terms and objects by dominant singular subspaces
so that user queries can be matched in a lower-rank semantic space.\par
This paper considers a third, intermediate approach to facilitate
the immediate detection of document (or term) clusters. We demonstrate
that both traditional sparse matrix reordering schemes (e.g., Reverse
Cuthill-McKee) and spectral-based approaches (e.g., Correspondence
Analysis or Fiedler vector-based spectral bisection) can be used
to permute original term by document (hypertext) matrices to a narrow-banded
form suitable for the detection of document (or term) clusters. Although
this approach would not exploit the higher-order semantic structure
in the database, it can be used to develop browsing tools for hypertext
and on-line information at a reduced computational cost. },
added-at = {2008-03-02T02:12:02.000+0100},
author = {Berry, Michael W. and Hendrickson, Bruce and Raghavan, Padma},
biburl = {https://www.bibsonomy.org/bibtex/2f0f56b69420c4af7df6395b3cd764237/dmartins},
classmath = {*68P20 Information storage and retrieval 05C50 Graphs and matrices
65F50 Sparse matrices 65F15 Eigenvalues (numerical linear algebra)},
description = {robotica-bib},
editor = {et al. Renegar, James},
interhash = {4120c33e9ba561ca553ed5292ba7011a},
intrahash = {f0f56b69420c4af7df6395b3cd764237},
isbn = {0-8218-0530-4/pbk},
journal = {Lect. Appl. Math.},
keywords = {decomposition; matrix reordering schemes singular sparse value},
language = {English},
note = {The mathematics of numerical analysis. 1995 AMS-SIAM summer seminar
in applied mathematics, July 17--August 11, 1995, Park City, UT,
USA. Providence },
pages = {99-123},
timestamp = {2008-03-02T02:12:18.000+0100},
title = {Sparse matrix reordering schemes for browsing hypertext.},
volume = 32,
year = 1996
}