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Incremental DC optimization algorithm for large-scale clusterwise linear regression
- Bagirov, Adil, Taheri, Sona, Cimen, Emre
Nonsmooth DC programming approach to the minimum sum-of-squares clustering problems
- Bagirov, Adil, Taheri, Sona, Ugon, Julien
A difference of convex optimization algorithm for piecewise linear regression
- Bagirov, Adil, Taheri, Sona, Asadi, Soodabeh
DC programming algorithm for clusterwise linear L1 regression
Aggregate subgradient method for nonsmooth DC optimization
- Bagirov, Adil, Taheri, Sona, Joki, Kaisa, Karmitsa, Napsu, Mäkelä, Marko
An augmented subgradient method for minimizing nonsmooth DC functions
- Bagirov, Adil, Hoseini Monjezi, Najmeh, Taheri, Sona
Robust piecewise linear L 1-regression via nonsmooth DC optimization
- Bagirov, Adil, Taheri, Sona, Karmitsa, Napsu, Sultanova, Nargiz, Asadi, Soodabeh
Nonsmooth optimization-based model and algorithm for semisupervised clustering
- Bagirov, Adil, Taheri, Sona, Bai, Fusheng, Zheng, Fangying
A novel optimization approach towards improving separability of clusters
- Bagirov, Adil, Hoseini-Monjezi, Najmeh, Taheri, Sona
Bundle enrichment method for nonsmooth difference of convex programming problems
- Gaudioso, Manilo, Taheri, Sona, Bagirov, Adil, Karmitsa, Napsu
Double bundle method for finding clarke stationary points in nonsmooth dc programming
- Joki, Kaisa, Bagirov, Adil, Karmitsa, Napsu, Makela, Marko, Taheri, Sona
Clusterwise support vector linear regression
- Joki, Kaisa, Bagirov, Adil, Karmitsa, Napsu, Mäkelä, Marko, Taheri, Sona
New diagonal bundle method for clustering problems in large data sets
- Karmitsa, Napsu, Bagirov, Adil, Taheri, Sona
Clustering in large data sets with the limited memory bundle method
- Karmitsa, Napsu, Bagirov, Adil, Taheri, Sona
Missing value imputation via clusterwise linear regression
- Karmitsa, Napsu, Taheri, Sona, Bagirov, Adil, Makinen, Pauliina
In this paper a new method of preprocessing incomplete data is introduced. The method is based on clusterwise linear regression and it combines two well-known approaches for missing value imputation: linear regression and clustering. The idea is to approximate missing values using only those data points that are somewhat similar to the incomplete data point. A similar idea is used also in clustering based imputation methods. Nevertheless, here the linear regression approach is used within each cluster to accurately predict the missing values, and this is done simultaneously to clustering. The proposed method is tested using some synthetic and real-world data sets and compared with other algorithms for missing value imputations. Numerical results demonstrate that the proposed method produces the most accurate imputations in MCAR and MAR data sets with a clear structure and the percentages of missing data no more than 25%
Nonsmooth optimization-based hyperparameter-free neural networks for large-scale regression
- Karmitsa, Napsu, Taheri, Sona, Joki, Kaisa, Paasivirta, Pauliina, Defterdarovic, J., Bagirov, Adil, Mäkelä, Marko
Methods and applications of clusterwise linear regression : a survey and comparison
- Long, Qiang, Bagirov, Adil, Taheri, Sona, Sultanova, Nargiz, Wu, Xue
Solving systems of nonlinear equations using a globally convergent optimization algorithm
- Taheri, Sona, Mammadov, Musa
Learning the naive bayes classifier with optimization models
- Taheri, Sona, Mammadov, Musa
Globally convergent algorithms for solving unconstrained optimization problems
- Taheri, Sona, Mammadov, Musa, Seifollahi, Sattar
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