Sparse Principal Component Analysis

@article{Zou2006SparsePC,
  title={Sparse Principal Component Analysis},
  author={Hui Zou and Trevor J. Hastie and Robert Tibshirani},
  journal={Journal of Computational and Graphical Statistics},
  year={2006},
  volume={15},
  pages={265 - 286}
}
Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the original variables, thus it is often difficult to interpret the results. We introduce a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified principal components with sparse loadings. We first show that PCA can be formulated as a regression… Expand
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