Unsupervised and Supervised Feature Selection for Incomplete Data via L_2,1-Norm and Reconstruction Error Minimization by Jun Cai, Linge Fan, Xin Xu, Xinrong Wu

“…Feature selection has been widely used in machine learning and data mining since it can alleviate the burden of the so-called curse of dimensionality of high-dimensional data. However, in previous works, researchers have designed feature selection methods with the assumption that all the information from a data set can be observed. …”
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Predicting multiple observations in complex systems through low-dimensional embeddings by Tao Wu, Xiangyun Gao, Feng An, Xiaotian Sun, Haizhong An, Zhen Su, Shraddha Gupta, Jianxi Gao, Jürgen Kurths

“…FRMM overcomes the curse of dimensionality and finds a generalized predictor, and thus has potential for applications in many other real-world systems.…”
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Explainable Artificial Intelligence Approach for the Early Prediction of Ventilator Support and Mortality in COVID-19 Patients by Nida Aslam

“…Nevertheless, the DL model does not suffer from the curse of dimensionality, but in order to identify significant attributes, the EAI feature importance method was used. …”
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Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: An Application to Cryptocurrencies by Inés Jiménez, Andrés Mora-Valencia, Trino-Manuel Ñíguez, Javier Perote

“…For this SNP-DCC model, we propose a stepwise procedure to compute pairwise conditional correlations under bivariate marginal SNP distributions, overcoming the curse of dimensionality. The procedure is compared to the assumption of dynamic equicorrelation (DECO), which is a parsimonious model when correlations among the assets are not significantly different but requires joint estimation of the multivariate SNP model. …”
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Variable surrogate model-based particle swarm optimization for high-dimensional expensive problems by Jie Tian, Mingdong Hou, Hongli Bian, Junqing Li

“…However, due to the curse of dimensionality and its implications, scaling SAEAs to high-dimensional expensive problems is still challenging. …”
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Evaluation and comparison of unsupervised methods for the extraction of spatial patterns from mass spectrometry imaging data (MSI) by Mridula Prasad, Geert Postma, Pietro Franceschi, Lutgarde M. C. Buydens, Jeroen J. Jansen

“…For high-dimensional MSI data, the curse of dimensionality also limits the performance of clustering methods which are usually overcome by pre-processing the data using dimension reduction techniques. …”
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Production control problem for multi-product multi-resource make-to-stock systems by Sinem Özkan, Önder Bulut, Mehmet Cemali Dinçer

“…Given the challenges posed by the curse of dimensionality in the value iteration algorithm, we suggest alternative heuristic production policies. …”
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Empowering optimal transport matching algorithm for the construction of surrogate parametric metamodel by Jacot Maurine, Champaney Victor, Torregrosa Jordan Sergio, Cortial Julien, Chinesta Francisco

“…However, interpolating in high-dimensional spaces encounters challenges stemming from the curse of dimensionality. The article offers insights into the application of OT, addressing associated challenges and proposing a novel methodology. …”
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Many-Objectives Optimization: A Machine Learning Approach for Reducing the Number of Objectives by António Gaspar-Cunha, Paulo Costa, Francisco Monaco, Alexandre Delbem

“…This problem is known as the curse of dimensionality. Simultaneously, the existence of many objectives, a characteristic of practical optimization problems, makes choosing a solution to the problem very difficult. …”
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A Projection Pursuit Dynamic Cluster Model for Tourism Safety Early Warning and Its Implications for Sustainable Tourism by Chenghao Zhong, Wengao Lou, Yongzeng Lai

“…Overall, the PPDC model can be adopted for tourism safety early warning with high-dimensional non-linear and non-normal distribution data modeling, as it overcomes the “curse of dimensionality” and the limitations associated with small sample sizes.…”
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Multi-Label Feature Selection Combining Three Types of Conditional Relevance by Lingbo Gao, Yiqiang Wang, Yonghao Li, Ping Zhang, Liang Hu

“…With the rapid growth of the Internet, the curse of dimensionality caused by massive multi-label data has attracted extensive attention. …”
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Robust deep semi-supervised learning with label propagation and differential privacy by Zhicong Yan, Shenghong Li, Zhongli Duan, Yuanyuan Zhao

“…However, they encounter troubles when applying to image classification followed by modern deep learning, since the diffusion algorithms face the curse of dimensionality. In this study, we propose a simple and efficient SSL method, combining a graph-based SSL paradigm with differential privacy. …”
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Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data. by Long Liu, Qingyu Meng, Cherry Weng, Qing Lu, Tong Wang, Yalu Wen

“…However, deep learning models generally suffer from the curse of dimensionality and the lack of biological interpretability, both of which have greatly limited their applications. …”
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A real‐time state estimation framework for integrated energy system considering measurement delay by Dongliang Xu, Junjun Xu, Zaijun Wu, Qinran Hu

“…A dynamic state estimation algorithm to enhance numerical stability is adopted to solve the problem that real‐time estimation based on the traditional Kalman filter suffers from the curse of dimensionality. Finally, a modified unscented Kalman filter (UKF) based estimation method is designed based on unified time processing and delay noise synthesizing. …”
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A Penalized Matrix Normal Mixture Model for Clustering Matrix Data by Jinwon Heo, Jangsun Baek

“…We extend their approach by regularizing further on the covariance to cope better with the curse of dimensionality for large size images. A penalized matrix normal mixture model with lasso-type penalty terms in both mean and covariance matrices is proposed, and then an expectation maximization algorithm is developed to estimate the parameters. …”
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Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms by Souad Larabi-Marie-Sainte

“…The curse of dimensionality problem occurs when the data are high-dimensional. …”
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Scalable and Privacy-Aware Online Learning of Nonlinear Structural Equation Models by Rohan Money, Joshin Krishnan, Baltasar Beferull-Lozano, Elvin Isufi

“…The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature approximation. …”
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Data Augmentation for Regression Machine Learning Problems in High Dimensions by Clara Guilhaumon, Nicolas Hascoët, Francisco Chinesta, Marc Lavarde, Fatima Daim

“…High-dimensional problems intrinsically involve the need for large amounts of data through the curse of dimensionality. That is why new approaches based on smart sampling techniques have been investigated to minimize the number of samples to be given to train the model, such as active learning methods. …”
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A Survey on Dimensionality Reduction Techniques for Time-Series Data by Mohsena Ashraf, Farzana Anowar, Jahanggir H. Setu, Atiqul I. Chowdhury, Eshtiak Ahmed, Ashraful Islam, Abdullah Al-Mamun

“…However, the “curse of dimensionality” often causes issues for learning approaches, which can fail to capture the temporal dependencies present in time-series data. …”
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A New Comparative Study of Dimensionality Reduction Methods in Large-Scale Image Retrieval by Mohammed Amin Belarbi, Saïd Mahmoudi, Ghalem Belalem, Sidi Ahmed Mahmoudi, Aurélie Cools

“…This problem is referred to as the ‘curse of dimensionality’. In the literature, several methods have been used to decrease the high dimension of features, including principal component analysis (PCA) and locality sensitive hashing (LSH). …”
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