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Found 8 papers, 0 papers with code
Discordance Minimization-based Imputation Algorithms for Missing Values in Rating Data
In this study, we propose analyses on missing value patterns using six real-world data sets in various applications, as well as the conditions for applicability of imputation algorithms.
A Mathematical Programming Approach for Integrated Multiple Linear Regression Subset Selection and Validation
The proposed model minimizes mean squared errors while ensuring that the majority of the important regression assumptions are met.
Optimization for L1-Norm Error Fitting via Data Aggregation
We propose a data aggregation-based algorithm with monotonic convergence to a global optimum for a generalized version of the L1-norm error fitting model with an assumption of the fitting function.
Subset Selection for Multiple Linear Regression via Optimization
For high dimensional cases, an iterative heuristic algorithm is proposed based on the mathematical programming models and a core set concept, and a randomized version of the algorithm is derived to guarantee convergence to the global optimum.
Bayesian Network Learning via Topological Order
We propose a mixed integer programming (MIP) model and iterative algorithms based on topological orders to solve optimization problems with acyclic constraints on a directed graph.
Iteratively Reweighted Least Squares Algorithms for L1-Norm Principal Component Analysis
Principal component analysis (PCA) is often used to reduce the dimension of data by selecting a few orthonormal vectors that explain most of the variance structure of the data.
An Aggregate and Iterative Disaggregate Algorithm with Proven Optimality in Machine Learning
We propose a clustering-based iterative algorithm to solve certain optimization problems in machine learning, where we start the algorithm by aggregating the original data, solving the problem on aggregated data, and then in subsequent steps gradually disaggregate the aggregated data.
Algorithms for Generalized Cluster-wise Linear Regression
We examine the performance of our algorithms on a stock keeping unit (SKU) clustering problem employed in forecasting halo and cannibalization effects in promotions using real-world retail data from a large supermarket chain.
