What Are Some Solutions to the Curse of Dimensionality in Cancer Research?
Several techniques can help mitigate the curse of dimensionality:
Feature Selection: Techniques such as LASSO, random forests, and mutual information can be used to select the most relevant features, reducing the dimensionality of the data. Dimensionality Reduction: Methods like Principal Component Analysis (PCA) and t-SNE help transform high-dimensional data into lower-dimensional spaces while preserving essential information. Regularization Techniques: Regularization methods such as ridge regression and dropout in neural networks can help prevent overfitting by adding constraints to the model. Advanced Machine Learning Algorithms: Algorithms like deep learning and ensemble methods are better suited for high-dimensional data and can help improve model performance.
