Many have suggested a bootstrap procedure for estimating the sampling variability of
principal component analysis (PCA) results. However, when the number of measurements …

In this article, we consider clustering based on principal component analysis (PCA) for high‐
dimensional mixture models. We present theoretical reasons why PCA is effective for …

Abstract The Manifold Hypothesis is a widely accepted tenet of Machine Learning which
asserts that nominally high-dimensional data are in fact concentrated near a low …

Principal component analysis is a popular dimension reduction technique often used to
visualize high‐dimensional data structures. In genomics, this can involve millions of …

Plots of principal component scores are a popular approach to visualize and explore high-
dimensional data. However, the inconsistency of high-dimensional eigenvectors prompted …

Principal component analysis (PCA) is a widely used technique for dimension reduction,
also for high-dimensional data. In the high-dimensional framework, PCA is not …

My thesis work focuses on aiding the practical implementation of advanced statistical
methods. Chapter 2 concerns the common practice of visual exploratory data analysis, and …

The technological developments of the last decades have made us able to generate
massive amounts of measurements, enhancing the need for data exploration. We often …

Aim Principal component analysis (PCA) is a widely used method for reducing the
dimension of high-dimensional data, even though the estimated eigenvectors are …