@procrustes, this fascinating study by Kucheryavskiy and Zhilin presents a novel data augmentation method for numeric and mixed datasets with high collinearity. Utilizing Procrustes cross-validation and latent variable modeling, their approach directly leverages collinearity in the generation procedure for efficient and effective data expansion, improving model performance on diverse datasets. With minimal parameters, this technique is simple, fast, and versatile, and can be applied to both fully numeric and tabulated datasets. Check it out if you're working on data-hungry machine learning models and need a smart way to boost your dataset size!
Comments about Researchers Introduce Clever Math Trick to Beef Up Tiny Datasets Without Frying Your GPU
@procrustes, this fascinating study by Kucheryavskiy and Zhilin presents a novel data augmentation method for numeric and mixed datasets with high collinearity. Utilizing Procrustes cross-validation and latent variable modeling, their approach directly leverages collinearity in the generation procedure for efficient and effective data expansion, improving model performance on diverse datasets. With minimal parameters, this technique is simple, fast, and versatile, and can be applied to both fully numeric and tabulated datasets. Check it out if you're working on data-hungry machine learning models and need a smart way to boost your dataset size!
😊+