6 papers
fdWasserstein: Optimal Transport Methods for Covariance Operators of Functional Data
V. Masarotto
Data increasingly arrive as collections of curves - a voice recording, a growth trajectory, a day of sensor readings - where each observation is a whole function rather than a sing…
In-flight pixel degradation of the Sentinel 5 Precursor TROPOMI-SWIR HgCdTe detector
Tim A. van Kempen, Marina Lobanova, Richard van Hees +4
The TROPOMI-SWIR HgCdTe detector on the Sentinel-5 Precursor mission has been performing in-orbit measurements of molecular absorption in Earth's atmosphere since its launch in Oct…
Tonal coarticulation revisited: functional covariance analysis to investigate the planning of co-articulated tones by Standard Chinese speakers
Valentina Masarotto, Yiya Chen
We aim to explain whether a stress memory task has a significant impact on tonal coarticulation. We contribute a novel approach to analyse tonal coarticulation in phonetics, where…
Covariance-based soft clustering of functional data based on the Wasserstein-Procrustes metric
V. Masarotto, G. Masarotto
We consider the problem of clustering functional data according to their covariance structure. We contribute a soft clustering methodology based on the Wasserstein-Procrustes dista…
Transportation-Based Functional ANOVA and PCA for Covariance Operators
Valentina Masarotto, Victor M. Panaretos, Yoav Zemel
We consider the problem of comparing several samples of stochastic processes with respect to their second-order structure, and describing the main modes of variation in this second…
Procrustes Metrics on Covariance Operators and Optimal Transportation of Gaussian Processes
Valentina Masarotto, Victor M. Panaretos, Yoav Zemel
Covariance operators are fundamental in functional data analysis, providing the canonical means to analyse functional variation via the celebrated Karhunen--Loève expansion. These…