Abstract
Correlation analysis has always been a key technique for understanding data. However, traditional methods are only applicable on the whole data set, providing only global information on correlations. Correlations usually have a local nature and two variables can be directly and inversely correlated at different points in the same data set. This situation arises typically in nonlinear processes. In this paper we propose a method to visualize the distribution of local correlations along the whole data set using dimension reduction mappings. The ideas are illustrated through an artificial data example.
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© 2002 Springer-Verlag Berlin Heidelberg
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Blanco, I.D., Vega, A.A.C., González, A.B.D. (2002). Correlation Visualization of High Dimensional Data Using Topographic Maps. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_163
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DOI: https://doi.org/10.1007/3-540-46084-5_163
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