Adaptive Reduction of Curse of Dimensionality in Nonparametric Instrumental Variable Estimation by Ming-Yueh Huang, Kwun Chuen Gary Chan

“…However, these estimators often face challenges from the curse of dimensionality in practice, as multi-dimensional covariates are common. …”
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Support Vector Machine – Recursive Feature Elimination for Feature Selection on Multi-omics Lung Cancer Data by Nuraina Syaza Azman, Azurah A Samah, Ji Tong Lin, Hairudin Abdul Majid, Zuraini Ali Shah, Nies Hui Wen, Chan Weng Howe

“…Multi-omics data is one of the biological data that exhibits high dimensionality, or more commonly known as the curse of dimensionality. The curse of dimensionality occurs when the dataset contains many features or attributes but with significantly fewer samples or observations. …”
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Neuro-dynamic Programming to Optimal Control of a Biotechnological Process by Tatiana Ilkova, Mitko Petrov

“…DP, however, has one major drawback, namely the “curse of dimensionality”. To overcome this shortcoming, an approach called neuro-dynamic programming (NDP) has been developed. …”
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A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning by Yuchen Fu, Quan Liu, Xionghong Ling, Zhiming Cui

“…A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of “curse of dimensionality,” which means that the states space will grow exponentially in the number of features and low convergence speed. …”
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Spatiotemporally Combined Dimensionality Reduction Algorithm for Optimizing Long-term Operation of Multi-reservoir Systems by CHEN Jia, ZHANG Hanjun, XU Nan

“…To alleviate the “curse of dimensionality” and improve the solution efficiency while ensuring the quality of solutions in optimizing the operation of multi-reservoir systems,this paper proposes a spatiotemporally combined dimensionality reduction algorithm which integrates and improves the dynamic programming with successive approximation (DPSA) and the progressive optimality algorithm (POA).First,a chain-based successive approximation strategy is proposed to expand the DPSA's optimization mode from “single reservoir alternation” to “cascade reservoir chain alternation”,which makes up for the DPSA's shortcomings in dealing with the hydraulic coupling relationships among cascade reservoirs.Then,a dynamic variable decoupling strategy and perturbation mechanism are proposed to deal with the POA's blind search problem and dimensionality problem.Finally,the two improved algorithms are combined,in which the improved POA is applied to solving the optimization problems of cascade reservoir chains under the framework of the improved DPSA.The power generation operation problem of the cascade reservoirs in the Yuan River Basin of Hunan Province and the classical ten-reservoir problem are utilized to test the performance of the proposed algorithm.The proposed algorithm outperforms seven existing alternatives in terms of solution quality and efficiency.The results indicate that the proposed algorithm can effectively alleviate the “curse of dimensionality” in optimizing the operation of multi-reservoir systems,improve the efficiency while ensuring the quality of solutions and has potential to be applied to optimizing the operation of complex large-scale multi-reservoir systems.…”
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Reinforcement learning algorithm based on minimum state method and average reward by LIU Quan1, FU Qi-ming1, GONG Sheng-rong1, FU Yu-chen1, CUI Zhi-ming1

“…In allusion to the problem that Q-Learning,which was used discount reward as the evaluation criterion,could not show the affect of the action to the next situation,AR-Q-Learning was put forward based on the average reward and Q-Learning.In allusion to the curse of dimensionality,which meant that the computational requirement grew exponen-tially with the number of the state variable.Minimum state method was put forward.AR-Q-Learning and minimum state method were used in reinforcement learning for Blocks World,and the result of the experiment shows that the method has the characteristic of aftereffect and converges more faster than Q-Learning,and at the same time,solve the curse of di-mensionality in a certain extent in Blocks World.…”
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On High-Dimensional Time-Variant Reliability Analysis with the Maximum Entropy Principle by Fuliang Zhou, Yu Hou, Hong Nie

“…The structural reliability analysis suffers from the curse of dimensionality if the associated limit state function involves a large number of inputs. …”
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Transmission scheduling scheme based on deep Q learning in wireless network by Jiang ZHU, Tingting WANG, Yonghui SONG, Yali LIU

“…To cope with the problem of data transmission in wireless networks,a deep Q learning based transmission scheduling scheme was proposed.The Markov decision process system model was formulated to describe the state transition of the system.The Q learning algorithm was adopted to learn and explore the system states transition information in the case of unknown system states transition probability to obtain the approximate optimal strategy of the schedule node.In addition,when the system state scale was big,the deep learning method was employed to map the relation between state and behavior to solve the problem of the large amount of computation and storage space in Q learning process.The simulation results show that the proposed scheme can approach the optimal strategy based on strategy iteration in terms of power consumption,throughput,packets loss rate.And the proposed scheme has a lower complexity,which can solve the problem of the curse of dimensionality.…”
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Task collaborative offloading scheme in vehicle multi-access edge computing network by Guanhua QIAO, Supeng LENG, Hao LIU, Kaisheng HUANG, Fan WU

“…In order to solve the problem that traditional mobile edge computing network can’t be straightforwardly applied to the Internet of vehicles (IoV) due to high speed mobility and dynamic network topology,a vehicular edge multi-access computing network (VE-MACN) was introduced to realize collaborative computing offloading between roadside units and smart vehicles.In this context,the collaborative computation offloading was formulated as a joint multi-access model selection and task assignment problem to realize the good balance between long-term system utility,diverse needs of IoV applications and energy consumption.Considering the complex joint optimization problem,a deep reinforcement learning-based collaborative computing offloading scheme was designed to overcome the curse of dimensionality for Q-learning algorithm.The simulation results demonstrate that the feasibility and effectiveness of proposed offloading scheme.…”
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Research on Text Similarity Measurement Hybrid Algorithm with Term Semantic Information and TF-IDF Method by Fei Lan

“…Because TF-IDF does not consider the semantic information of words, it cannot accurately reflect the similarity between texts, and semantic information enhanced methods distinguish between text documents poorly because extended vectors with semantic similar terms aggravate the curse of dimensionality. Aiming at this problem, this paper advances a hybrid with the semantic understanding and TF-IDF to calculate the similarity of texts. …”
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An Improved Differential Evolution Solution for Software Project Scheduling Problem by A. C. Biju, T. Aruldoss Albert Victoire, Kumaresan Mohanasundaram

“…The interest on finding a more efficient solution technique for SPSP is always a topic of interest due to the fact of ever growing challenges faced by the software industry. The curse of dimensionality is introduced in the scheduling problem by ever increasing software assignments and the number of staff who handles it. …”
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Planning of Cascade Hydropower Stations with the Consideration of Long-Term Operations under Uncertainties by Changjun Wang, Shutong Chen

“…To address the curse of dimensionality caused by the long-term stochastic operations, we further propose a novel dimensionality reduction approach based on dual equilibrium to transform the multistage model into a tractable two-stage stochastic program. …”
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Multiview Discriminative Geometry Preserving Projection for Image Classification by Ziqiang Wang, Xia Sun, Lijun Sun, Yuchun Huang

“…However, this simple concatenation strategy not only ignores the complementary nature of different views, but also ends up with “curse of dimensionality.” To address this problem, we propose a novel multiview subspace learning algorithm in this paper, named multiview discriminative geometry preserving projection (MDGPP) for feature extraction and classification. …”
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A hybrid model for smart grid theft detection based on deep learning by Yinling LIAO, Jincan LI, Bing WANG, Jun ZHANG, Yaoyuan LIANG

“…A hybrid deep learning model was proposed to effectively detect electricity theft in smart grids.The hybrid model employed a deep learning convolutional neural network (AlexNet) to tackle the curse of dimensionality, significantly enhancing data processing accuracy and efficiency.It further improved classification accuracy by differentiating between normal and abnormal electricity usage using adaptive boosting (AdaBoost).To resolve the issue of class imbalance, undersampling techniques were utilized, ensuring balanced performance across various data classes.Additionally, the artificial bee colony algorithm was used to optimize hyperparameters for both AdaBoost and AlexNet, effectively boosting overall model performance.The effectiveness of this hybrid model was evaluated using real smart meter datasets from an electricity company.Compared to similar models, this hybrid model achieves accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and area under the curve-receiver operating characteristic curve (AUC-ROC) scores of 88%, 86%, 84%, 85%, 78%, and 91%, respectively.The proposed model not only increases the accuracy of electricity usage monitoring, but also offers a new perspective for intelligent analysis in power systems.…”
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Hybrid Unsupervised Exploratory Plots: A Case Study of Analysing Foreign Direct Investment by Álvaro Herrero, Alfredo Jiménez, Secil Bayraktar

“…The curse of dimensionality has been an open issue for many years and still is, as finding nonobvious and previously unknown patterns in ever-increasing amounts of high-dimensional data is not an easy task. …”
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Validation and Calibration of an Agent-Based Model: A Surrogate Approach by Yi Zhang, Zhe Li, Yongchao Zhang

“…However, this can result in the “curse of dimensionality” phenomenon and decrease the robustness of the model’s output. …”
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An Enhanced Sine Cosine Algorithm for Feature Selection in Network Intrusion Detection by zahra asgari varzaneh, soodeh hosseini

“…However, the curse of dimensionality presents a challenge because there are so many dimensions in the data. …”
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Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization by Qinghua Gu, Xuexian Li, Song Jiang

“…In general, the metaheuristic algorithms for solving such problems often suffer from the “curse of dimensionality.” In order to improve the disadvantage of Grey Wolf Optimizer when solving the LSGO problems, three genetic operators are embedded into the standard GWO and a Hybrid Genetic Grey Wolf Algorithm (HGGWA) is proposed. …”
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Comparison of Machine Learning Classification Methods for Determining the Geographical Origin of Raw Milk Using Vibrational Spectroscopy by Aimen El Orche, Amine Mamad, Omar Elhamdaoui, Amine Cheikh, Miloud El Karbane, Mustapha Bouatia

“…In this exploratory study, midinfrared spectroscopy combined with machine learning classification methods was investigated as a rapid and nondestructive method for the classification of milk according to its geographical origin. The curse of dimensionality makes some classification methods struggle to train efficient models. …”
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Interpreting convolutional neural networks' low-dimensional approximation to quantum spin systems by Yilong Ju, Shah Saad Alam, Jonathan Minoff, Fabio Anselmi, Han Pu, Ankit Patel

“…However, it remains uncertain how CNNs, with a model complexity that scales at most linearly with the number of particles, solve the “curse of dimensionality” and efficiently represent wavefunctions in exponentially large Hilbert spaces. …”
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