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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.