Latent semantic indexing is an optimal special case of multidimensional scaling
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SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
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Communications of the ACM - Special issue on information filtering
Information Processing and Management: an International Journal
Latent semantic indexing: a probabilistic analysis
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
A semidiscrete matrix decomposition for latent semantic indexing information retrieval
ACM Transactions on Information Systems (TOIS)
A similarity-based probability model for latent semantic indexing
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Scientific Computing
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IRREGULAR '98 Proceedings of the 5th International Symposium on Solving Irregularly Structured Problems in Parallel
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NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLPWorkshop on Automatic summarization - Volume 4
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Telcordia LSI Engine: Implementation and Scalability Issues
RIDE '01 Proceedings of the 11th International Workshop on research Issues in Data Engineering
ISICT '03 Proceedings of the 1st international symposium on Information and communication technologies
Locality preserving indexing for document representation
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Knowledge management technology
IBM Systems Journal
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Natural Language Engineering
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Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Multi-label informed latent semantic indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Latent semantic analysis for multiple-type interrelated data objects
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Similarity of Semantic Relations
Computational Linguistics
Access Structures for Angular Similarity Queries
IEEE Transactions on Knowledge and Data Engineering
The uncovering of hidden structures by Latent Semantic Analysis
Information Sciences: an International Journal
Regularized locality preserving indexing via spectral regression
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Augmenting the power of LSI in text retrieval: Singular value rescaling
Data & Knowledge Engineering
Modeling hidden topics on document manifold
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Proceedings of the 2009 conference on Information Modelling and Knowledge Bases XX
Clustered sub-matrix singular value decomposition
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
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Journal of Artificial Intelligence Research
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EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
From frequency to meaning: vector space models of semantics
Journal of Artificial Intelligence Research
Augmenting the power of the various versions of LSI used in document retrieval
DNIS'05 Proceedings of the 4th international conference on Databases in Networked Information Systems
Fast dimension reduction for document classification based on imprecise spectrum analysis
Information Sciences: an International Journal
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We present a novel algorithm that creates document vectors with reduced dimensionality. This work was motivated by an application characterizing relationships among documents in a collection. Our algorithm yielded inter-document similarities with an average precision up to 17.8% higher than that of singular value decomposition (SVD) used for Latent Semantic Indexing. The best performance was achieved with dimensional reduction rates that were 43% higher than SVD on average. Our algorithm creates basis vectors for a reduced space by iteratively “scaling” vectors and computing eigenvectors. Unlike SVD, it breaks the symmetry of documents and terms to capture information more evenly across documents. We also discuss correlation with a probabilistic model and evaluate a method for selecting the dimensionality using log-likelihood estimation.