Multivariate Descriptive Statistical Analysis
Multivariate Descriptive Statistical Analysis
Using Correspondence Analysis to Combine Classifiers
Machine Learning
Mining Open Answers in Questionnaire Data
IEEE Intelligent Systems
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Effective Feature Space Reduction with Imbalanced Data for Semantic Concept Detection
SUTC '08 Proceedings of the 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008)
Information Sciences: an International Journal
The structure of narrative: The case of film scripts
Pattern Recognition
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Correspondence Analysis (CA) is a statistical method aiming at the graphical representation of the contingencies between the rows and the columns of a categorical data set. A critical step of the CA algorithm is the Singular Value Decomposition (SVD) analysis of a coded matrix. The size of this matrix affects drastically the analysis computational cost. As the size of the matrix increases, the method becomes computationally expensive or even impossible. In this paper we propose an alternative scheme that overpasses this limitation, without affecting the results accuracy. A set of Monte Carlo simulations and real data applications showed the efficiency of the proposed approach over the standard one, especially in the case of "tall" data sets.