Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems by Reisinger, C, Zhang, Y

“…This implies that DNNs can break the curse of dimensionality in numerical approximations and optimal control of PDEs and SPDEs. …”

The n^th-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (n^th-CASAM-N): Mathematical Framework by Dan Gabriel Cacuci

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Illustrative Application of the nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems to the Nordheim–Fuchs Reactor Dynamics/Safety Model by Dan Gabriel Cacuci

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A Machining State-Based Approach to Tool Remaining Useful Life Adaptive Prediction by Yiming Li, Xiangmin Meng, Zhongchao Zhang, Guiqiu Song

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Minimalistic Logit Model as an Effective Tool for Predicting the Risk of Financial Distress in the Visegrad Group by Michal Pavlicko, Jaroslav Mazanec

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An enhanced mechanism for detection of spam emails by deep learning technique with bio-inspired algorithm by Neomi Nelin Nicholas, V. Nirmalrani

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Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding by Hong Liu, Kewen Xia, Tiejun Li, Jie Ma, Eunice Owoola

Subjects: “…curse of dimensionality…”
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An Unsupervised Feature Extraction Using Endmember Extraction and Clustering Algorithms for Dimension Reduction of Hyperspectral Images by Sayyed Hamed Alizadeh Moghaddam, Saeed Gazor, Fahime Karami, Meisam Amani, Shuanggen Jin

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Spatial Spectral Band Selection for Enhanced Hyperspectral Remote Sensing Classification Applications by Ruben Moya Torres, Peter W.T. Yuen, Changfeng Yuan, Johathan Piper, Chris McCullough, Peter Godfree

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Learned Practical Guidelines for Evaluating Conditional Entropy and Mutual Information in Discovering Major Factors of Response-vs.-Covariate Dynamics by Ting-Li Chen, Hsieh Fushing, Elizabeth P. Chou

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How deep convolutional neural networks lose spatial information with training by Umberto M Tomasini, Leonardo Petrini, Francesco Cagnetta, Matthieu Wyart

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Machine Learning and Integrative Analysis of Biomedical Big Data by Bilal Mirza, Wei Wang, Jie Wang, Howard Choi, Neo Christopher Chung, Peipei Ping

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Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in the Upstream of Development of Novel Energy Technologies by Sergei Manzhos, Manabu Ihara

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A Hierarchical Gamma Mixture Model-Based Method for Classification of High-Dimensional Data by Muhammad Azhar, Mark Junjie Li, Joshua Zhexue Huang

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An Efficient and Fast Sparse Grid Algorithm for High-Dimensional Numerical Integration by Huicong Zhong, Xiaobing Feng

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Computation of High-Order Sensitivities of Model Responses to Model Parameters—I: Underlying Motivation and Current Methods by Dan Gabriel Cacuci

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On the Need to Determine Accurately the Impact of Higher-Order Sensitivities on Model Sensitivity Analysis, Uncertainty Quantification and Best-Estimate Predictions by Dan Gabriel Cacuci

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The n^th-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (n^th-CASAM-L): I. Mathematical Framew... by Dan Gabriel Cacuci

Subjects: “…sensitivity analysis and the curse of dimensionality…”
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Clustering high dimensional data using RIA by Aziz, Nazrina

“…We notice that, it can obtain cluster easily and hence avoid the curse of dimensionality.It is also manage to cluster large data sets with mixed numeric and categorical value.…”

Genetically Optimized UFLANN for Uncovering Clusters by Himanshu Dutta, Saurabh Bilgaiyan, Bhabani Shankar Prasad Mishra, Satchidananda Dehuri, Ashish Ghosh

“…As a result, UFLANN introduces the problem of curse of dimensionality in the given datasets. However, it has been realized that the problems like sparse data and distance concentration associated with curse of dimensionality cast this problem to again a very complex problem. …”
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