Abstract. In this work, we describe a procedure to visualize nonlinear process dynamics using a self-organizing map based local model dynamical estimator. The proposed method exploits the topology preserving nature of the resulting estimator to extract visualizations (planes) of insightful dynamical features, that allow to explore nonlinear systems whose behavior changes with the operating point. Since the visualizations are obtained from a dynamical model of the process, measures on the goodness of this estimator (such as RMSE or AIC) are also applicable as a measure of the trustfulness of the visualizations. To illustrate the application of the proposed method, an experiment to analyze the dynamics of a nonlinear system on different operating points is included.
We present an application of interactive dimensionality reduction (DR) for exploratory analysis of gene expression data that produces two lively updated projections, a sample map and a gene map, by rendering intermediate results of a t-SNE. The user can condition the projections "on the fly" by subsets of genes or samples, so updated views reveal coexpression patterns for different cancer types or gene groups. * This work is part of Grant PID2020-115401GB-I00 funded by MCIN/AEI/ 10.13039/501100011033. The results shown here are based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
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