From explained variance of correlated components to PCA without orthogonality constraints
<|reference_start|>From explained variance of correlated components to PCA without orthogonality constraints: Block Principal Component Analysis (Block PCA) of a data matrix A, where loadings Z are determined by maximization of AZ 2 over unit norm orthogonal loadings, is difficult to use for the design of sparse PCA by 1 regularization, due to the difficulty of taking care of both the orthogonality constraint on loadings and the non differentiable 1 penalty. Our objective in this paper is to relax the orthogonality constraint on loadings by introducing new objective functions expvar(Y) which measure the part of the variance of the data matrix A explained by correlated components Y = AZ. So we propose first a comprehensive study of mathematical and numerical properties of expvar(Y) for two existing definitions Zou et al. [2006], Shen and Huang [2008] and four new definitions. Then we show that only two of these explained variance are fit to use as objective function in block PCA formulations for A rid of orthogonality constraints.<|reference_end|>
@article{chavent2024from,
    title={From explained variance of correlated components to PCA without
  orthogonality constraints},
    author={Marie Chavent (IMB), Guy Chavent},
    journal={arXiv preprint arXiv:2402.04692},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04692},
    primaryClass={stat.ML cs.LG}
}