Collision prediction based q-learning for mobile robot navigation in unknown dynamic environments by Findi, Ahmed H. M., Marhaban, Mohammad Hamiruce, Raja Ahmad, Raja Mohd Kamil, Hassan, Mohd Khair

“…However, its salient downside is the curse of dimensionality problem, where it incurs a huge computational power and memory requirement. …”
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Feature selection optimization using hybrid relief-f with self-adaptive differential evolution by Zainudin, Muhammad Noorazlan Shah, Sulaiman, Md. Nasir, Mustapha, Norwati, Perumal, Thinagaran, Ahmad Nazri, Azree Shahrel, Mohamed, Raihani, Abd Manaf, Syaifulnizam

“…In various classification areas, the curse of dimensionality becomes a major challenge among the researchers. …”
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A robust structure identification method for evolving fuzzy system by Sa'ad, Hisham Haider Yusef, Nor Ashidi, Mat Isa, Ahmed, M. M., Sa'da, Adnan Haider Yusef

“…First, RSIM provides a solution for the curse of dimensionality. Second, RSIM can also be applied to low-dimensional problems. …”
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Joint Learning of Correlation-Constrained Fuzzy Clustering and Discriminative Non-Negative Representation for Hyperspectral Band Selection by Zelin Li, Wenhong Wang

“…Hyperspectral band selection plays an important role in overcoming the curse of dimensionality. Recently, clustering-based band selection methods have shown promise in the selection of informative and representative bands from hyperspectral images (HSIs). …”
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Smooth Trajectory Planning at the Handling Limits for Oval Racing by Levent Ögretmen, Matthias Rowold, Marvin Ochsenius, Boris Lohmann

“…Graph-based trajectory planning methods can find the global discrete-optimal solution, but they suffer from the curse of dimensionality. Therefore, to achieve low computation times despite a long planning horizon, coarse discretization and simple edges that are efficient to generate must be used. …”
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Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution by Frederic Jacquelin, Jungyun Bae, Bo Chen, Darrell Robinette, Pruthwiraj Santhosh, Troy Kraemer, Bonnie Henderson

“…While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. …”
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Efficient Spatiotemporal Graph Search for Local Trajectory Planning on Oval Race Tracks by Matthias Rowold, Levent Ögretmen, Tobias Kerbl, Boris Lohmann

“…A considerable challenge of search-based methods in a spatiotemporal domain is the curse of dimensionality. Therefore, we propose how a previously presented graph structure that is based on intervals instead of discrete values can be searched more efficiently without losing optimality by using a uniform-cost search strategy. …”
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OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data by Xiaoyu Zhang, Yuting Xing, Kai Sun, Yike Guo

“…Nevertheless, it is challenging to capture them from the genome-wide data, due to the large number of molecular features and small number of available samples, which is also called “the curse of dimensionality” in machine learning. To tackle this problem and pave the way for machine learning-aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. …”
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Contested logistics simulation output analysis with approximate dynamic programming: a proposed methodology by Matthew Powers, Brian O'Flynn

“…Findings – This study demonstrates simulation output data as a means of state–space reduction to mitigate the curse of dimensionality. Furthermore, massive amounts of simulation output data become unwieldy. …”
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A Tent Lévy Flying Sparrow Search Algorithm for Wrapper-Based Feature Selection: A COVID-19 Case Study by Qinwen Yang, Yuelin Gao, Yanjie Song

“…The “Curse of Dimensionality” induced by the rapid development of information science might have a negative impact when dealing with big datasets, and it also makes the problems of symmetry and asymmetry increasingly prominent. …”
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Medical big data: promise and challenges by Choong Ho Lee, Hyung-Jin Yoon

“…Medical big data analyses are complicated by many technical issues, such as missing values, curse of dimensionality, and bias control, and share the inherent limitations of observation study, namely the inability to test causality resulting from residual confounding and reverse causation. …”
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An Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data by Zihao Li, Liumei Zhang

“…In industrial automation, datasets are often high-dimensional, meaning an effort to study all dimensions directly leads to data sparsity, thus causing outliers to be masked by noise effects in high-dimensional spaces. The “curse of dimensionality” phenomenon renders many conventional outlier detection methods ineffective. …”
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A Fast-Converging Kernel Density Estimator for Dispersion in Horizontally Homogeneous Meteorological Conditions by Gunther Bijloos, Johan Meyers

“…Their main disadvantage is that they suffer from the curse of dimensionality, i.e., they converge at a rate of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4</mn><mo>/</mo><mo>(</mo><mi>d</mi><mo>+</mo><mn>4</mn><mo>)</mo></mrow></semantics></math></inline-formula> with <i>d</i> the number of dimensions. …”
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Review of Stochastic Dynamic Vehicle Routing in the Evolving Urban Logistics Environment by Nikola Mardešić, Tomislav Erdelić, Tonči Carić, Marko Đurasević

“…Although potent, these approaches become restrictive due to the “curse of dimensionality”. Sacrificing granularity for scalability, researchers have opted for aggregation and decomposition techniques to overcome this problem and recent approaches explore solutions using deep learning. …”
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CNN-AdaBoost based hybrid model for electricity theft detection in smart grid by Santosh Nirmal, Pramod Patil, Jambi Ratna Raja Kumar

“…The paper proposed a hybrid method that deals with different issues like the curse of dimensionality, data imbalance problem and also study existing models which give low theft detection rate. …”
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Spectrally Segmented-Enhanced Neural Network for Precise Land Cover Object Classification in Hyperspectral Imagery by Touhid Islam, Rashedul Islam, Palash Uddin, Anwaar Ulhaq

“…However, challenges persist in object classification in hyperspectral imagery or hyperspectral image classification, including the curse of dimensionality, data redundancy, overfitting, and computational costs. …”
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Remaining Useful Life Prognosis for Turbofan Engine Using Explainable Deep Neural Networks with Dimensionality Reduction by Chang Woo Hong, Changmin Lee, Kwangsuk Lee, Min-Seung Ko, Dae Eun Kim, Kyeon Hur

“…The first is the requirement of numerous sensors for different components, i.e., the curse of dimensionality. Second, the deep neural network cannot identify the problematic component of the turbofan engine due to its “black box” property. …”
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Research on Ensemble Learning-Based Feature Selection Method for Time-Series Prediction by Da Huang, Zhaoguo Liu, Dan Wu

“…The computational expenses associated with traditional methodologies in managing such data dimensions, coupled with vulnerability to the curse of dimensionality, further compound the challenges at hand. …”
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Adaptive Power Control Based on Double-layer Q-learning Algorithm for Multi-parallel Power Conversion Systems in Energy Storage Station by Yile Wu, Le Ge, Xiaodong Yuan, Xiangyun Fu, Mingshen Wang

“…In addition, existing Q-learning algorithms for adaptive power allocation suffer from the curse of dimensionality. To overcome these challenges, an adaptive power control method based on the double-layer Q-learning algorithm for <tex>$n$</tex> parallel PCSs of the ESS is proposed in this paper. …”
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Enhanced Human Activity Recognition Using Wearable Sensors via a Hybrid Feature Selection Method by Changjun Fan, Fei Gao

“…Considering the issues of limited resources of wearable devices and the curse of dimensionality, it is vital to generate the best feature combination which maximizes the performance and efficiency of the following mapping from feature subsets to activities. …”
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