The Geometry of Nodal Sets and Outlier Detection
This addresses the problem of outlier detection in spectral geometry for researchers in mathematics and data analysis, but it is incremental as it builds on existing eigenfunction theory with limited general understanding.
The paper introduces a function derived from Laplacian eigenfunctions that appears effective for detecting anomalous points on manifolds, with rigorous results shown on the unit square and Paley graphs, and numerical evidence suggesting broader applicability.
Let $(M,g)$ be a compact manifold and let $-Δφ_k = λ_k φ_k$ be the sequence of Laplacian eigenfunctions. We present a curious new phenomenon which, so far, we only managed to understand in a few highly specialized cases: the family of functions $f_N:M \rightarrow \mathbb{R}_{\geq 0}$ $$ f_N(x) = \sum_{k \leq N}{ \frac{1}{\sqrt{λ_k}} \frac{|φ_k(x)|}{\|φ_k\|_{L^{\infty}(M)}}}$$ seems strangely suited for the detection of anomalous points on the manifold. It may be heuristically interpreted as the sum over distances to the nearest nodal line and potentially hints at a new phenomenon in spectral geometry. We give rigorous statements on the unit square $[0,1]^2$ (where minima localize in $\mathbb{Q}^2$) and on Paley graphs (where $f_N$ recovers the geometry of quadratic residues of the underlying finite field $\mathbb{F}_p$). Numerical examples show that the phenomenon seems to arise on fairly generic manifolds.