1 citations · 1 across the 5 of their papers we have counts for
5 papers
spca: An R package to Compute Least Squares Sparse Principal Components
Giovanni Maria Merola
This paper introduces the R package spca, which provides a computational framework for least squares sparse principal component analysis (LS-SPCA). Unlike other SPCA methods, LS-SP…
Blocked Cross-Validation: A Precise and Efficient Method for Hyperparameter Tuning
Giovanni Maria Merola
Hyperparameter tuning plays a crucial role in optimizing the performance of predictive learners. Cross--validation (CV) is a widely adopted technique for estimating the error of di…
Sparse Principal Components Analysis: a Tutorial
Giovanni Maria Merola
The topic of this tutorial is Least Squares Sparse Principal Components Analysis (LS SPCA) which is a simple method for computing approximated Principal Components which are combin…
Projection Sparse Principal Component Analysis: an efficient least squares method
Giovanni Maria Merola
We propose a new sparse principal component analysis (SPCA) method in which the solutions are obtained by projecting the full cardinality principal components onto subsets of varia…
Sparse Principal Component Analysis: a Least Squares approximation approach
Giovanni Maria Merola
Sparse Principal Components Analysis aims to find principal components with few non-zero loadings. We derive such sparse solutions by adding a genuine sparsity requirement to the o…