Sparse Principal Component Analysis on Breast cancer gene expression dataset p = 500, n = 89, r = 3
35.2Explained Variance Ratio (FVE)PCA
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| PCASparsity (k1, k2, k3)=(500, 500, 500)2026.07 | 35.2 | 10 | — | |
| msPCA::mspca (Σ)Sparsity (k1, k2, k3)=(20, 20, 20), Input=Covariance matrix (Σ)2026.07 | 9.3 | 7.5 | 27.472 | |
| msPCA::mspca (X)Sparsity (k1, k2, k3)=(20, 20, 20), Input=Data matrix (X)2026.07 | 9.3 | 7.5 | 13.382 | |
| nsprcomp::nsprcompSparsity (k1, k2, k3)=(20, 20, 20)2026.07 | 8.4 | 10 | 0.027 | |
| nsprcomp::nscumcompSparsity (k1, k2, k3)=(33, 22, 5)2026.07 | 8.2 | 10 | 0.152 | |
| mixOmics::spcaSparsity (k1, k2, k3)=(20, 20, 20)2026.07 | 7.7 | 10 | 0.031 | |
| amanpg::spca.amanpgSparsity (k1, k2, k3)=(96, 4, 2)2026.07 | 6.7 | 0.063 | 0.252 | |
| sparsepca::spcaSparsity (k1, k2, k3)=(46, 13, 8)2026.07 | 6.1 | 10 | 4.752 | |
| sklearn.decomposition.SparsePCASparsity (k1, k2, k3)=(56, 3, 6)2026.07 | 5.7 | 10 | 0.18 | |
| elasticnet::spca (Σ)Sparsity (k1, k2, k3)=(20, 20, 20), Input=Covariance matrix (Σ)2026.07 | 4.3 | 0.04 | 18.13 | |
| elasticnet::spca (X)Sparsity (k1, k2, k3)=(20, 20, 20), Input=Data matrix (X)2026.07 | 4.3 | 0.021 | 0.904 | |
| PMA::SPCSparsity (k1, k2, k3)=(14, 17, 27)2026.07 | 2.8 | 0.001 | 0.042 |