Interpretable linear dimensionality reduction based on bias-variance analysis

• Published in: Data mining and knowledge discovery Vol. 38; no. 4; pp. 1713 - 1781
• Main Authors: Bonetti, Paolo, Metelli, Alberto Maria, Restelli, Marcello
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Bias; Chemistry and Earth Sciences; Collinearity; Computer Science; Data Mining and Knowledge Discovery; Datasets; Design; Design analysis; Information Storage and Retrieval; Machine learning; Physics; Statistics for Engineering; Synthetic data; Variance analysis; Dimensionality reduction; Bias-variance tradeoff; Feature aggregation; Linear regression

One of the central issues of several machine learning applications on real data is the choice of the input features. Ideally, the designer should select a small number of the relevant, nonredundant features to preserve the complete information contained in the original dataset, with little collinear...