Details

Autor(en) / Beteiligte

• Song, Jiayuan
• Liu, Zheng

Titel

Comparison of Norm-Based Feature Selection Methods on Biological Omics Data

Ist Teil von

• Proceedings of the 5th International Conference on Advances in Image Processing, 2021, p.109-112

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2021

Quelle

ACM Digital Library

Beschreibungen/Notizen

• Feature selection methods have become significant methods when analyzing high-throughput biological data due to the nature of large p and small n problems. One of the most crucial categories of feature selection methods is norm-based approaches because they can reduce the magnitude of coefficients and enhance the sparsity of selected features. There are many norm-based feature selection methods with different merits and demerits. Therefore, the specific choice of norm-based methods for omics data has become a problem. In our work, we mainly concentrate on the comparison and evaluation of two popular norm-based methods, namely Lasso and Ridge regression. The regression with norm is Lasso Regression and the regression with norm is Ridge Regression. The results indicate that Ridge Regression performs better than Lasso Regression when dealing with high throughput TCGA datasets.