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Table 6 Variation in classification accuracy as a function of parameters for consensus-PCA on gene-expression data

From: Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

Dataset ϕ A c c ( X ̃ P C A S ) ϕ A c c ( X ̃ P C A U S )
  M = 200 M = 500 M = 1000 M = 200 M = 500 M = 1000
Prostate Tumor 97.06 97.06 97.06 100 100 100
Breast Cancer Relapse 57.89 63.16 57.89 57.89 57.89 52.63
Lung Cancer 99.33 99.33 99.33 96.64 95.97 96.64
Lymphoma 94.12 97.06 97.06 76.47 67.65 61.76
  1. Classification accuracies for testing cohorts of 4 different binary class gene-expression datasets for X ̃ P C A S and X ̃ P C A U S , while varying the number of subsets M generated within CreateEmbed.
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