Principles of information structure common to six levels of the human cognitive system
Information Sciences: an International Journal
On modeling of information retrieval concepts in vector spaces
ACM Transactions on Database Systems (TODS)
Information Processing and Management: an International Journal - The Potential for Improvments in Commerical Document Retrieval Systems
Some inconsistencies and misnomers in probabilistic information retrieval
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
Information Processing and Management: an International Journal
Foundations of statistical natural language processing
Foundations of statistical natural language processing
A similarity-based probability model for latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Computer Methods for Mathematical Computations
Computer Methods for Mathematical Computations
A probabilistic model for latent semantic indexing in information retrieval and filtering
Computational information retrieval
Using Linear Algebra for Intelligent Information Retrieval
Using Linear Algebra for Intelligent Information Retrieval
A decision-theoretic approach to the evaluation of information retrieval systems
Information Processing and Management: an International Journal
The uncovering of hidden structures by Latent Semantic Analysis
Information Sciences: an International Journal
Search structures and algorithms for personalized ranking
Information Sciences: an International Journal
Intrinsic dimension estimation of manifolds by incising balls
Pattern Recognition
A context-based model for Knowledge Management embodied in work processes
Information Sciences: an International Journal
Efficient retrieval of ontology fragments using an interval labeling scheme
Information Sciences: an International Journal
Automatic text categorization based on content analysis with cognitive situation models
Information Sciences: an International Journal
Computing with words for text processing: An approach to the text categorization
Information Sciences: an International Journal
Information retrieval performance enhancement using the average standard estimator and the multi-criteria decision weighted set of performance measures
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Information retrieval today is much more challenging than traditional small document retrieval. The main difference is the importance of correlations between related concepts in complex data structures. As collections of data grow and contain more entries, they require more complex relationships, links, and groupings between individual entries. This paper introduces two novel methods for estimating data intrinsic dimensionality based on the singular value decomposition (SVD). The average standard estimator (ASE) and the multi-criteria decision weighted model are used to estimate matrix intrinsic dimensionality for large document collections. The multi-criteria weighted model calculates the sum of weighted values of matrix dimensions which demonstrated best performance using all possible dimensions [1]. ASE estimates the level of significance for singular values that resulted from the singular value decomposition. ASE assumes that those variables with deep relations have sufficient correlation and that only those relationships with high singular values are significant and should be maintained [1]. Experimental results indicate that ASE improves precision and relative relevance for MEDLINE document collection by 10.2% and 12.9% respectively compared to the percentage of variance dimensionality estimation. Results based on testing three document collections over all possible dimensions using selected performance measures indicate that ASE improved matrix intrinsic dimensionality estimation by including the effect of both singular values magnitude of decrease and random noise distracters. The multi-criteria weighted model with dimensionality reduction provides a more efficient implementation for information retrieval than using a full rank model.