Efficient Dimensionality Reduction Strategies for Quantum Reinforcement Learning by Eva Andres, M. P. Cuellar, G. Navarro

“…However, despite their potential, there are still open questions such as barren plateau phenomenon and the challenges of scalability and the curse of dimensionality, which become particularly relevant in Reinforcement Learning (RL) when working in environments with high-dimensional state and action spaces. …”
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Optimal Proactive Caching for Multi-View Streaming Mobile Augmented Reality by Zhaohui Huang, Vasilis Friderikos

“…To tackle the curse of dimensionality of the optimization problem, a nominal long short-term memory (LSTM) neural network is proposed, which is trained offline with optimal solutions and provides high-quality real-time decision making within a gap between 5.6% and 9.8% during inference. …”
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A Non-myopic and Fast Resource Scheduling Algorithm for Multi-target Tracking of Space-based Radar Considering Optimal Integrated Performance by Zengfu WANG, Guangyu YANG, Shuling JIN

“…To deal with the curse of dimensionality caused by the continuous state space, continuous action space and continuous observation space, we use the online POMDP algorithm based on the Monte Carlo Tree Search (MCTS) and partially observable Monte Carlo planning with observation widening algorithm. …”
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Interpretability in neural networks towards universal consistency by Dionéia Motta Monte-Serrat, Carlo Cattani

“…Neurolinguistic principles that link interpretation to language and cognition; the semantic dimension that arises not only from the linguistic system, but also from the context in which the information is produced; and the theoretical bases for understanding language as a 'form' (process) and not as a substance (set of signs) provide the groundwork for the intelligent systems’ improvement so that they have universal consistency and lessen the effects of the ‘curse of dimensionality’ or of the bias in the interpretation by the system. …”
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Generating synthetic multidimensional molecular time series data for machine learning: considerations by Gary An, Chase Cockrell

“…We argue the insufficiency of statistical and data-centric machine learning (ML) means of generating this type of synthetic data is due to a combination of factors: perpetual data sparsity due to the Curse of Dimensionality, the inapplicability of the Central Limit Theorem in terms of making assumptions about the statistical distributions of this type of data, and the inability to use ab initio simulations due to the state of perpetual epistemic incompleteness in cellular/molecular biology. …”
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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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Classification of polarimetric SAR images using compact convolutional neural networks by Mete Ahishali, Serkan Kiranyaz, Turker Ince, Moncef Gabbouj

“…The traditional Machine Learning (ML) methods proposed in this domain generally focus on utilizing highly discriminative features to improve the classification performance, but this task is complicated by the well-known “curse of dimensionality” phenomena. Other approaches based on deep Convolutional Neural Networks (CNNs) have certain limitations and drawbacks, such as high computational complexity, an unfeasibly large training set with ground-truth labels, and special hardware requirements. …”
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Predicting Instructors Performance in Higher Education Systems by Dr. K. Kalaiselvi, J. Sowmiya

“…Prior works carried in data mining algorithms like J48 Decision Tree, Multilayer Perception, Naïve Bayes, and Sequential Minimal Optimization impose issues like the curse of dimensionality, cardinality and imbalance attributes. …”
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Improved Robot Path Planning Method Based on Deep Reinforcement Learning by Huiyan Han, Jiaqi Wang, Liqun Kuang, Xie Han, Hongxin Xue

“…However, persistent challenges remain, including the curse of dimensionality, difficulties of model convergence and sparsity in rewards. …”
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Ensemble and Greedy Approach for the Reconstruction of Large Gene Co-Expression Networks by Francisco Gómez-Vela, Fernando M. Delgado-Chaves, Domingo S. Rodríguez-Baena, Miguel García-Torres, Federico Divina

“…Due to the increasing amount of available data, computational methods for networks generation must deal with the so-called curse of dimensionality in the quest for the reliability of the obtained results. …”
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Low-cost yield-driven design of antenna structures using response-variability essential directions and parameter space reduction by Anna Pietrenko-Dabrowska, Slawomir Koziel, Lukasz Golunski

“…The involvement of surrogate modeling techniques is the most common approach to alleviating these difficulties, yet conventional modeling methods suffer to a great extent form the curse of dimensionality. This work proposes a technique for low-cost yield optimization of antenna structures. …”
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Spatiotemporal Transformer Neural Network for Time-Series Forecasting by Yujie You, Le Zhang, Peng Tao, Suran Liu, Luonan Chen

“…Predicting high-dimensional short-term time-series is a difficult task due to the lack of sufficient information and the curse of dimensionality. To overcome these problems, this study proposes a novel spatiotemporal transformer neural network (STNN) for efficient prediction of short-term time-series with three major features. …”
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Enhancing hyperspectral remote sensing image classification using robust learning technique by Alaa Ali Hameed

“…However, the abundance of data present in HSS also poses the challenge called the curse of dimensionality. The reduction of data dimensionality is crucial before applying any machine learning model to achieve optimal results. …”
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Cooperative coevolutionary surrogate ensemble-assisted differential evolution with efficient dual differential grouping for large-scale expensive optimization problems by Rui Zhong, Enzhi Zhang, Masaharu Munetomo

“…Inspired by RDG2 and RDG3, we design the adaptive determination threshold and further decompose relatively large-scale sub-components to alleviate the curse of dimensionality. In the optimization phase, the SEADE is adopted as the basic optimizer, where the global and the local surrogate model are constructed by generalized regression neural network (GRNN) with all historical samples and Gaussian process regression (GPR) with recent samples. …”
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Two‐step attribute reduction for AIoT networks by Chao Ren, Gaoxin Lyu, Xianmei Wang, Yao Huang, Wei Li, Lei Sun

“…The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. …”
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A Binary Multi-Objective Chimp Optimizer With Dual Archive for Feature Selection in the Healthcare Domain by Jayashree Piri, Puspanjali Mohapatra, Manas Ranjan Pradhan, Biswaranjan Acharya, Tapas Kumar Patra

“…As a result, the curse of dimensionality affects learning from a medical dataset to discover significant characteristics, making it necessary to minimize the feature set. …”
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A tutorial on the art of dynamic programming for some issues concerning Bellman’s principle of optimality by Eiji Mizutani, Stuart Dreyfus

“…As a result, we show that our artful choice of state description not only renders the principle valid in each problem, but also makes each DP as efficient as the standard DP that solves a shortest-path problem in the same DAG, circumventing successfully the so-called curse of dimensionality, a price to be paid frequently by state enlargement. …”
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Interpretable Single-dimension Outlier Detection (ISOD): An Unsupervised Outlier Detection Method Based on Quantiles and Skewness Coefficients by Yuehua Huang, Wenfen Liu, Song Li, Ying Guo, Wen Chen

“…Existing outlier detection algorithms, which can be divided into supervised methods, semi-supervised methods, and unsupervised methods, suffer from missing labeled data, the curse of dimensionality, low interpretability, etc. To address these issues, in this paper, we present an unsupervised outlier detection method based on quantiles and skewness coefficients called ISOD (Interpretable Single dimension Outlier Detection). …”
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A Hybrid Intrusion Detection Model Combining SAE with Kernel Approximation in Internet of Things by Yukun Wu, Wei William Lee, Xuan Gong, Hui Wang

“…Owing to the constraints of time and space complexity, network intrusion detection systems (NIDSs) based on support vector machines (SVMs) face the “curse of dimensionality” in a large-scale, high-dimensional feature space. …”
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An engineering model for 3-D turbulent wind inflow based on a limited set of random variables by M. Fluck, C. Crawford

“…This is a major issue for stochastic methods, which suffer from the <q>curse of dimensionality</q> leading to a steep performance drop with an increasing number of random variables contained in the governing equations. …”
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