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A Randomized Rounding Algorithm for Sparse PCA
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DOI：10.1145/3046948.png)
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Abstract
En 中文
We present and analyze a simple, two-step algorithm to approximate the optimal solution of the sparse PCA problem. In the proposed approach, we first solve an l(1)-penalized version of the NP-hard sparse PCA optimization problem and then we use a randomized rounding strategy to sparsify the resulting dense solution. Our main theoretical result guarantees an additive error approximation and provides a tradeoff between sparsity and accuracy. Extensive experimental evaluation indicates that the proposed approach is competitive in practice, even compared to state-of-the-art toolboxes such as Spasm.
Keywords:
Sparce pca
rounding randomized algorithm
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