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Table 1 Running times (in seconds) of different SPCA algorithms

From: Stochastic convex sparse principal component analysis

Sample size Cvx-SPCA [9] [12] [11]
n=50 k 20.9 207.1 48.7 3002
n=100k 26.2 466.9 78.3 3237.4
n=500 k 35.6 2737.06 2661.7 5276.93
n=1m 35.8 3408.59 3568 5274.26
  1. Since proposed Cvx-SPCA does not depend on eigenvalue decomposition or semi-definite programming, it is more scalable in terms of the sample size. It also requires less iterations to reach a desired sparsity
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