A comparison of ℓ1-regularizion, PCA, KPCA and ICA for dimensionality reduction in logistic regression

@article{Musa2014ACO,
  title={A comparison of ℓ1-regularizion, PCA, KPCA and ICA for dimensionality reduction in logistic regression},
  author={Abdallah Bashir Musa},
  journal={Int. J. Machine Learning & Cybernetics},
  year={2014},
  volume={5},
  pages={861-873}
}
Relevant information extraction and dimensionality reduction of the original input features is an interesting research area in machine learning and data analysis. Logistic regression (LR) is a well-known classification method that has been used widely in many applications of data mining, machine learning, and bioinformatics. However, its performance is affected by the multi-co-linearity among its predictors, and the features’ redundancy. ‘1-regularizion and features extraction methods are… CONTINUE READING

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