A randomized algorithm based on the basic SDP relaxation for sparse PCA achieves an approximation ratio bounded by the sparsity constant with high probability and O(log d) on average under a technical assumption satisfied for low-rank or exponentially decaying eigenvalue SDP solutions.
Principal component analysis for special types of data
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A Randomized Algorithm for Sparse PCA based on the Basic SDP Relaxation
A randomized algorithm based on the basic SDP relaxation for sparse PCA achieves an approximation ratio bounded by the sparsity constant with high probability and O(log d) on average under a technical assumption satisfied for low-rank or exponentially decaying eigenvalue SDP solutions.