2 documents found, page 1 of 1
An empirical comparison of two approaches for CDPCA in high-dimensional data
Freitas, Adelaide; Macedo, Eloísa; Vichi, MaurizioModifed principal component analysis techniques, specially those yielding sparse solutions, are attractive due to its usefulness for interpretation purposes, in particular, in high-dimensional data sets. Clustering and disjoint principal component analysis (CDPCA) is a constrained PCA that promotes sparsity in the loadings matrix. In particular, CDPCA seeks to describe the data in terms of disjoint (and possibl...
Recent Developments in Modeling and Applications in Statistics
Oliveira, Paulo Eduardo; Temido, Maria da Graça; Henriques, Carla; Vichi, MaurizioStatistics has been a main tool in almost every field of activity and an essential part of applied scientific work, supporting conclusions and offering insights into new uses for established methodologies, thus making it a valuable resource in looking for faceless facts. Model construction describing populations or phenomena subject to randomness use a wide range of methods. Data collection provides the basis f...