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The main focus is to estimate functional principal components (FPCs), which are only nonzero in subregions and are referred to as sparse FPCs. These sparse FPCs not only represent the major variation sources but also can be used to identify the subregions where those major variations exist.
Sep 11, 2013 · We propose a unified approach to regularized PCA which can induce both sparsity and smoothness in both the row and column principal components.
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Aug 5, 2016 · In this article, we propose a supervised sparse and functional principal component (SupSFPC) framework that can incorporate supervision information to recover ...
Missing: Components | Show results with:Components
The focus of this dissertation is on functional data which are sparsely and irregularly observed. Such data require special consideration, as classical ...
The functional approach views them as sparse and noisy realizations of an underlying smooth process and aims to study the modes of variations of the process.
Abstract: Existing functional principal component analysis (FPCA) methods are restricted to data with a single or finite number of random functions (much ...
Functional principal component analysis (FPCA) is a statistical method for investigating the dominant modes of variation of functional data.
Nov 1, 2021 · We introduce a sparse multivariate functional principal component analysis method by incorporating ideas from the group sparse maximum ...
Jul 8, 2021 · FPCA works with very sparse data, does not require the same number of observations for each subject, and does not demand that the observations ...
This work proposes a unified approach to regularized PCA which can induce both sparsity and smoothness in both the row and column principal components, ...