Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization by Rajeev Goel, Sandhya Bansal, Kavita Gupta

“…However, it struggles with “curse of dimensionality” due to excessive features resulting in prolonged training time and exhaustive computational demand. …”
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Sampling with Prior Knowledge for High-dimensional Gravitational Wave Data Analysis by He Wang, Zhoujian Cao, Yue Zhou, Zong-Kuan Guo, Zhixiang Ren

“…Extracting knowledge from high-dimensional data has been notoriously difficult, primarily due to the so-called "curse of dimensionality" and the complex joint distributions of these dimensions. …”
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Cost-efficient behavioral modeling of antennas by means of global sensitivity analysis and dimensionality reduction by Slawomir Koziel, Anna Pietrenko-Dabrowska

“…Challenges include the curse of dimensionality and the high nonlinearity of antenna characteristics. …”
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An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals by Lorenzo Manoni, Claudio Turchetti, Laura Falaschetti

“…However finding appropriate models from high-dimensional data sampled from biosignals is in general unpracticable due to the problem known as the “curse of dimensionality”. Dimensionality reduction, that is representing data in some lower-dimensional space, is the commonly adopted technique to handle these data. …”
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Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks by Mikhail Krivonosov, Tatiana Nazarenko, Vadim Ushakov, Daniil Vlasenko, Denis Zakharov, Shangbin Chen, Oleg Blyus, Alexey Zaikin

“…SGNNs are particularly good for addressing the challenges posed by high-dimensional datasets, particularly in healthcare, where traditional machine learning and Artificial Intelligence methods often struggle to find global optima due to the “curse of dimensionality”. To apply Geometric Deep Learning we propose a synolitic or ensemble graph representation of the data, a universal method that transforms any multidimensional dataset into a network, utilising only class labels from training data. …”
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Short-Term Optimal Scheduling Method of Cascade Hydropower Stations Based on Segmented Load Construction Strategy by LI Chonghao, LI Shushan, TANG Hongbing, LIU Huan, MA Xiangyu, LIAO Shengli

“…With the rapid development of China's economy,electricity demand has increased.As the largest clean energy in China,hydropower has been put into large-scale and centralized production,and the most complex large-scale hydropower system in the world is formed.However,short-term hydropower optimal scheduling faces the serious challenge of the curse of dimensionality due to massive cascade hydropower stations,numerous hydropower constraints,and time-sensitive scheduling requirements.Therefore,this paper proposes a short-term optimal scheduling method for cascade hydropower stations based on a segmented load construction strategy.First,the selection rules of the hydropower stations to be regulated are formulated from the perspective of the regulation performance,location,and task of the cascade hydropower stations.Then,a method is given to divide the complex original load into two sub-load processes,namely the segmented load process with time reduction scale and the residual load process with frequent fluctuations.Finally,the adjustment method is given,and the particle swarm algorithm is used for traversal and optimization at different stages,so as to find the load division process when the target is optimal.By taking seven cascade hydropower stations in a certain river basin in southwest China as research objects,a mature particle swarm algorithm is used to simulate the typical day during the flood and dry periods.The results show that the segmented load construction strategy proposed in this paper can effectively reduce the solution complexity,and the appropriate number of segments can improve the solution efficiency while ensuring the solution quality.In addition,this method greatly reduces the output fluctuation of auxiliary hydropower stations and helps to reduce the difficulty of scheduling and unit loss.Therefore,the method proposed in this paper can provide a reference for solving the short-term scheduling of large-scale cascade hydropower stations and is of good practical value.…”
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Ensemble of semi-supervised feature selection algorithms to reinforce heuristic function in ant colony optimization by Fereshteh Karimi, Mohammad Bagher Dowlatshahi, Amin Hashemi

“…Feature selection (FS) is a well-known dimensionality reduction method that chooses a hopeful subset of the original feature collection to diminish the influence the curse of dimensionality phenomenon. FS improves learning performance by removing irrelevant and redundant features. …”
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A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study by Aoyu Li, Jingwen Li, Yishan Hu, Yan Geng, Yan Qiang, Juanjuan Zhao

“…To address the challenges (eg, the curse of dimensionality and increased model complexity) posed by high-dimensional features, we developed a dynamic adaptive feature selection optimization algorithm to identify the most impactful subset of features for classification performance. …”
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