Choosing LSI dimensions by document linear association analysis

Shi, C; Lu, J

Choosing LSI dimensions by document linear association analysis

Shi, C Lu, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Conference on Information and Knowledge Engineering, 2003, 2 pp. 615 - 621
Issue Date:: 2003-12-01

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Field	Value	Language
dc.contributor.author	Shi, C	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2003-12-01	en_US
dc.identifier.citation	Proceedings of the International Conference on Information and Knowledge Engineering, 2003, 2 pp. 615 - 621	en_US
dc.identifier.isbn	1932415076	en_US
dc.identifier.uri	http://hdl.handle.net/10453/6848
dc.description.abstract	Latent Semantic Indexing (LSI) has proven to be a valuable analysis tool with a wide range of applications, however the crucial question, choosing an appropriate number of dimensions for LSI, is still unsolved. In this paper, a new method which is to deal with this problem is described. It finds that a sum of total dot products between all document vectors reaches the maximum value at a specific number of dimensions for a given dataset. With this reduced dimensions LSI achieves the best performance. The performance evaluations have demonstrated that this method can choose an appropriate number of dimensions for LSI and effective detect the data structure for a dataset.	en_US
dc.relation.ispartof	Proceedings of the International Conference on Information and Knowledge Engineering	en_US
dc.title	Choosing LSI dimensions by document linear association analysis	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080105 Expert Systems	en_US
dc.location.activity	Las Vegas, Nevada, USA	en_US
dc.location.activity	Palm Springs, California, USA
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

Latent Semantic Indexing (LSI) has proven to be a valuable analysis tool with a wide range of applications, however the crucial question, choosing an appropriate number of dimensions for LSI, is still unsolved. In this paper, a new method which is to deal with this problem is described. It finds that a sum of total dot products between all document vectors reaches the maximum value at a specific number of dimensions for a given dataset. With this reduced dimensions LSI achieves the best performance. The performance evaluations have demonstrated that this method can choose an appropriate number of dimensions for LSI and effective detect the data structure for a dataset.

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http://hdl.handle.net/10453/6848