Biobjective Sparse Principal Component Analysis Articles uri icon

publication date

  • November 2014

start page

  • 151

end page

  • 159

volume

  • 132

International Standard Serial Number (ISSN)

  • 0047-259X

Electronic International Standard Serial Number (EISSN)

  • 1095-7243

abstract

  • Principal Components are usually hard to interpret. Sparseness is considered as one way
    to improve interpretability, and thus a trade-off between variance explained by the com-
    ponents and sparseness is frequently sought. In this note we address the problem of si-
    multaneous maximization of variance explained and sparseness, and a heuristic method
    is proposed. It is shown that recent proposals in the literature may yield dominated so-
    lutions, in the sense that other components, found with our procedure, may exist which
    explain more variance and at the same time are sparser

keywords

  • principal component analysis; mixed integer nonlinear programming; biobjective optimization; sparseness