Web: http://arxiv.org/abs/2206.11424

June 24, 2022, 1:10 a.m. | Haixu Wang, Jiguo Cao

cs.LG updates on arXiv.org arxiv.org

Using representations of functional data can be more convenient and
beneficial in subsequent statistical models than direct observations. These
representations, in a lower-dimensional space, extract and compress information
from individual curves. The existing representation learning approaches in
functional data analysis usually use linear mapping in parallel to those from
multivariate analysis, e.g., functional principal component analysis (FPCA).
However, functions, as infinite-dimensional objects, sometimes have nonlinear
structures that cannot be uncovered by linear mapping. Linear methods will be
more overwhelmed given …

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