GTM: the generative topographic mapping
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
Self-Organizing Maps
Process Monitoring and Modeling Using the Self-Organizing Map
Integrated Computer-Aided Engineering
RBF principal manifolds for process monitoring
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
Dimensionality Problem in the Visualization of Correlation-Based Data
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
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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.