Robust PCA and classification in biosciences
Bioinformatics
Nonparametric density estimation by exact leave-p-out cross-validation
Computational Statistics & Data Analysis
Robust PCA for skewed data and its outlier map
Computational Statistics & Data Analysis
Automatic model selection by cross-validation for probabilistic PCA
Neural Processing Letters
A resistant learning procedure for coping with outliers
Annals of Mathematics and Artificial Intelligence
Fast robust estimation of prediction error based on resampling
Computational Statistics & Data Analysis
A novel multi-view learning developed from single-view patterns
Pattern Recognition
| Hi-index | 0.03 |
Cross-validation (CV) is a very popular technique for model selection and model validation. The general procedure of leave-one-out CV (LOO-CV) is to exclude one observation from the data set, to construct the fit of the remaining observations and to evaluate that fit on the item that was left out. In classical procedures such as least-squares regression or kernel density estimation, easy formulas can be derived to compute this CV fit or the residuals of the removed observations. However, when high-breakdown resampling algorithms are used, it is no longer possible to derive such closed-form expressions. High-breakdown methods are developed to obtain estimates that can withstand the effects of outlying observations. Fast algorithms are presented for LOO-CV when using a high-breakdown method based on resampling, in the context of robust covariance estimation by means of the MCD estimator and robust principal component analysis. A robust PRESS curve is introduced as an exploratory tool to select the number of principal components. Simulation results and applications on real data show the accuracy and the gain in computation time of these fast CV algorithms.