Jul 4, 2007 · We discuss applications in subset selection and sparse recovery and show on artificial examples and biological data that our algorithm does ...
This simple fact allows us to derive sufficient global optimality conditions for the original sparse PCA problem.
Schur-Horn theorem: the diagonal of a matrix majorizes its eigenvalues. Other simple solution: Thresholding, compute the first factor x from regular PCA and ...
We discuss applications in subset selection and sparse recovery and show on artificial examples and biological data that our algorithm does provide globally.
We discuss applications in subset selection and sparse recovery and show on artificial examples and biological data that our algorithm does provide globally ...
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A new semidefinite relaxation is formulated and a greedy algorithm is derived that computes a full set of good solutions for all target numbers of non zero ...
This is known as sparse principal component analysis and has a wide array of applications in machine learning and engineering. We formulate a new semidefinite ...
Optimal Solutions for Sparse Principal Component Analysis Sivan Sabato and Shai Shalev-Shwartz ; Journal: Journal of Machine Learning Research, ; Volume: 9 ; URL: ...
Aug 30, 2017 · Principal component analysis (PCA) is a method for finding linear combinations of features, called principal components, that correspond to the ...
Sparse principal component analysis (PCA) addresses the problem of finding a lin- ear combination of the variables in a given data set with a sparse ...