Using ℓ1-Relaxation and Integer Programming to Obtain Dual Bounds for Sparse PCA

Using ℓ1-Relaxation and Integer Programming to Obtain Dual Bounds for Sparse PCA

<jats:p> Dual Bounds of Sparse Principal Component Analysis </jats:p><jats:p> Sparse principal component analysis (PCA) is a widely used dimensionality reduction tool in machine learning and statistics. Compared with PCA, sparse PCA enhances the interpretability by incorporating a...

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Bibliographic Details
Main Authors: Dey, Santanu S, Mazumder, Rahul, Wang, Guanyi
Other Authors: Massachusetts Institute of Technology. Operations Research Center
Format: Article
Language:English
Published: Institute for Operations Research and the Management Sciences (INFORMS) 2022
Online Access:https://hdl.handle.net/1721.1/144219

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