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On the Convergence of the Dynamic Inner PCA Algorithm

By Sungho Shin and others
Dynamic inner principal component analysis (DiPCA) is a powerful method for the analysis of time-dependent multivariate data. DiPCA extracts dynamic latent variables that capture the most dominant temporal trends by solving a large-scale, dense, and nonconvex nonlinear program (NLP). A scalable decomposition algorithm has been recently proposed in the literature... Show more
March 12, 2020
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On the Convergence of the Dynamic Inner PCA Algorithm
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