Numerical Approximation of Andrews Plots with Optimal Spatial-Spectral Smoothing
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Andrews plots provide aesthetically pleasant visualizations of high-dimensional datasets. This work proves that Andrews plots (when defined in terms of the principal component scores of a dataset) are optimally “smooth” on average, and solve an infinite-dimensional quadratic minimization program over the set of linear isometries from the Euclidean data space to L^2([0,1]). By building technical machinery that characterizes the solutions to general infinite-dimensional quadratic minimization programs over linear isometries, we further show that the solution set is (in the generic case) a manifold. To avoid the ambiguities presented by this manifold of solutions, we add “spectral smoothing” terms to the infinite-dimensional optimization program to induce Andrews plots with optimal spatial-spectral smoothing. We characterize the (generic) set of solutions to this program and prove that the resulting plots admit efficient numerical approximations. These spatial-spectral smooth Andrews plots tend to avoid some “visual clutter” that arises due to the oscillation of trigonometric polynomials.
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