5 papers
Beyond Explained Variance: A Cautionary Tale of PCA
Gionni Marchetti
We address shortcomings of principal component analysis (PCA) for visualizing high-dimensional data lying on a nonlinear low-dimensional manifold via two-dimensional scatterplots,…
Learning the Intrinsic Dimensionality of Fermi-Pasta-Ulam-Tsingou Trajectories: A Nonlinear Approach using a Deep Autoencoder Model
Gionni Marchetti
We address the intrinsic dimensionality (ID) of high-dimensional trajectories, comprising data points, of the Fermi-Pasta-Ulam-Tsingou (FPUT) model with $N…
Intrinsic Dimensionality of Fermi-Pasta-Ulam-Tsingou High-Dimensional Trajectories Through Manifold Learning: A Linear Approach
Gionni Marchetti
A data-driven approach based on unsupervised machine learning is proposed to infer the intrinsic dimension of the high-dimensional trajectories of the Fermi-Pasta-Ulam-T…
A Machine Learning Tool to Analyse Spectroscopic Changes in High-Dimensional Data
Alberto Martinez-Serra, Gionni Marchetti, Francesco D'Amico +4
When nanoparticles (NPs) are introduced into a biological solution, layers of biomolecules form on their surface, creating a corona. Understanding how the structure of the protein…
Metric Similarity and Manifold Learning of Circular Dichroism Spectra of Proteins
Gionni Marchetti
We present a machine learning analysis of circular dichroism spectra of globular proteins from the SP175 database, using the optimal transport-based -Wasserstein distance $\math…