Applied multivariate techniques
Applied multivariate techniques
Generalized vector spaces model in information retrieval
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Text retrieval and filtering: analytic models of performance
Text retrieval and filtering: analytic models of performance
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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
A vector space model for automatic indexing
Communications of the ACM
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Modern Information Retrieval
Approximate Dimension Equalization in Vector-based Information Retrieval
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
RANDOM '02 Proceedings of the 6th International Workshop on Randomization and Approximation Techniques
Eigenvalue-based estimators for optimal dimensionality reduction in information retrieval
Eigenvalue-based estimators for optimal dimensionality reduction in information retrieval
Model-averaged latent semantic indexing
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Information Processing and Management: an International Journal
An empirical study of required dimensionality for large-scale latent semantic indexing applications
Proceedings of the 17th ACM conference on Information and knowledge management
An analysis of latent semantic term self-correlation
ACM Transactions on Information Systems (TOIS)
Kernel latent semantic analysis using an information retrieval based kernel
Proceedings of the 18th ACM conference on Information and knowledge management
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In this study amended parallel analysis (APA), a novel method for model selection in unsupervised learning problems such as information retrieval (IR), is described. At issue is the selection of k, the number of dimensions retained under latent semantic indexing (LSI). Amended parallel analysis is an elaboration of Horn's parallel analysis, which advocates retaining eigenvalues larger than those that we would expect under term independence. Amended parallel analysis operates by deriving confidence intervals on these “null” eigenvalues. The technique amounts to a series of nonparametric hypothesis tests on the correlation matrix eigenvalues. In the study, APA is tested along with four established dimensionality estimators on six standard IR test collections. These estimates are evaluated with regard to two IR performance metrics. Additionally, results from simulated data are reported. In both rounds of experimentation APA performs well, predicting the best values of k on 3 of 12 observations, with good predictions on several others, and never offering the worst estimate of optimal dimensionality. © 2005 Wiley Periodicals, Inc.