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Table 1 Feature selection through singular vectors (SVs) in data 2

From: Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data

 

1st SV (η (1))

2nd SV (η (2))

3rd SV (η (3))

η 1

0.98 (0.002)

0.00 (0.018)

0.00 (0.001)

η 2

0.00 (0.003)

0.21 (0.033)

0.00 (0.001)

η 3

0.00 (0.001)

0.00 (0.010)

0.22 (0.029)

η 4

0.22 (0.013)

0.00 (0.017)

0.00 (0.005)

η 5

0.00 (0.000)

0.98 (0.004)

0.00 (0.005)

η 6

0.00 (0.004)

0.00 (0.002)

0.98 (0.003)

 

1st SV (μ (1))

2nd SV (μ (2))

3rd SV (μ (3))

μ 1

0.99 (0.005)

0.01 (0.022)

0.01 (0.014)

μ 2

0.01 (0.027)

0.99 (0.004)

0.01 (0.015)

μ 3

0.01 (0.024)

0.01 (0.018)

0.99 (0.003)

μ 4

0.01 (0.023)

0.01 (0.026)

0.01 (0.017)

  1. These results show mean weight coefficients (standard deviation) in 100 simulation runs. Significant weight coefficients are bold faced
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BMC Bioinformatics

ISSN: 1471-2105

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