Extrapolation-based tuning parameters selection in massive data analysis

Robust Principal Component Analysis with Adaptive Selection for Tuning Parameters

The present paper discusses robustness against outliers in a principal component analysis (PCA). We propose a class of procedures for PCA based on the minimum psi principle, which unifies various approaches, including the classical procedure and recently proposed procedures. The reweighted matrix algorithm for off-line data and the gradient algorithm for on-line data are both investigated with ...

Consistent selection of tuning parameters via variable selection stability

Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature, their performances largely depend on the tuning parameters that balance the trade-off between model fitting and model sparsity. Existing tuning criteria mainly follow the route of minimizing the e...

Supplier Selection in Supply Chain Management by Data Envelopment Analysis

Nowadays, managing a supply chain is turned to be one of the fundamentals of business process.  In doing so, investigating and analyzing each and every of the processes and selecting the best of each process is an important challenge for strategic managers. In this paper Data Envelopment Analysis (DEA) technique is used and a model is provided for selecting the best suppliers with flexible inpu...

Automatic selection of tuning parameters in wind power prediction

A Monte Carlo-Based Search Strategy for Dimensionality Reduction in Performance Tuning Parameters

Redundant and irrelevant features in high dimensional data increase the complexity in underlying mathematical models. It is necessary to conduct pre-processing steps that search for the most relevant features in order to reduce the dimensionality of the data. This study made use of a meta-heuristic search approach which uses lightweight random simulations to balance between the exploitation of ...