ALR-HT: A fast and efficient Lasso regression without hyperparameter tuning.

Area of Science:

• Machine Learning
• Statistical Modeling
• Data Science

Background:

• Lasso regression is effective for high-dimensional data and feature selection.
• Hyperparameter tuning in Lasso regression can be time-consuming and sensitive to noisy data, especially in big data contexts.

Purpose of the Study:

• To introduce a novel additive Lasso regression method that eliminates the need for hyperparameter tuning.
• To analyze the generalization bounds and learning rates of the proposed method.
• To demonstrate the algorithm's effectiveness and versatility in regularized regression.

Main Methods:

• Integration of Markov resampling with additive models to create additive Lasso regression without hyperparameter tuning (ALR-HT).
• Estimation of generalization bounds and establishment of fast learning rates for ALR-HT.
• Experimental evaluation on benchmark datasets comparing ALR-HT with other algorithms.

Main Results:

• ALR-HT demonstrates superior performance in terms of sampling and training time.
• The proposed method achieves a lower mean squared error (MSE) compared to existing algorithms.
• ALR-HT shows versatility and effectiveness when applied to Ridge regression.

Conclusions:

• The developed ALR-HT algorithm provides an efficient and effective alternative for high-dimensional regression tasks.
• ALR-HT overcomes the limitations of traditional Lasso regression regarding hyperparameter tuning and noisy data.
• The method's adaptability extends to other regularized regression techniques.