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Fig. 8 | BMC Bioinformatics

Fig. 8

From: Sparse Proteomics Analysis – a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

Fig. 8

Accuracies of sparse classifiers from SPA, Lasso, and ℓ 1-SVM on the real pancreatic cancer data-sets. While Lasso and ℓ 1-SVM achieve better classification accuracy with increasing number of features, SPA is particularly well suited for the “very-sparse regime” where only few features (<20) are used for classification

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BMC Bioinformatics

ISSN: 1471-2105

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