The sparse generalized eigenvalue problem (SGEP) aims to find the leading eigenvector
with sparsity structure. SGEP plays an important role in statistical learning and has wide …

We present a novel technique for sparse principal component analysis. This method, named
Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA), is based …

Continuous and atlas-free analysis of brain structural connectivity Page 1 The Annals of Applied
Statistics 2024, Vol. 18, No. 3, 1815–1839 https://doi.org/10.1214/23-AOAS1858 © Institute of …

We consider the problem of extracting joint and individual signals from multi-view data, that
is, data collected from different sources on matched samples. While existing methods for …

Abstract Background The Gross Motor Function Classification System (GMFCS) is a widely
used tool for assessing the mobility of people with Cerebral Palsy (CP). It classifies patients …

Motivation Ordinal classification problems arise in a variety of real-world applications, in
which samples need to be classified into categories with a natural ordering. An example of …

Ordinal class labels are frequently observed in classification studies across various fields. In
medical science, patients' responses to a drug can be arranged in the natural order …

Generalized eigenvalue problems (GEPs) find applications in various fields of science and
engineering. For example, principal component analysis, Fisher's discriminant analysis, and …

We present a novel technique for sparse principal component analysis. This method, named
eigenvectors from eigenvalues sparse principal component analysis (EESPCA), is based on …

We propose methods to estimate sufficient reductions in matrix-valued predictors for
regression or classification. We assume that the first moment of the predictor matrix given the …