Dimensionality reduction method for hyperspectral image analysis based on rough set theory
European Journal of Remote Sensing, 2020 High-dimensional features often cause computational complexity and dimensionality curse. Feature selection and feature extraction are the two mainstream methods for dimensionality reduction.
Zhenhua Wang +5 more
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A deep learning technique Alexnet to detect electricity theft in smart grids
Frontiers in Energy Research, 2023 Electricity theft (ET), which endangers public safety, creates a problem with the regular operation of grid infrastructure and increases revenue losses. Numerous machine learning, deep learning, and mathematical-based algorithms are available to find ET.
Nitasha Khan +10 more
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Can Shallow Neural Networks Beat the Curse of Dimensionality? A Mean Field Training Perspective [PDF]
IEEE Transactions on Artificial Intelligence, 2020 We prove that the gradient descent training of a two-layer neural network on empirical or population risk may not decrease population risk at an order faster than $t^{-4/(d-2)}$ under mean field scaling.
Stephan Wojtowytsch, E. Weinan
semanticscholar +1 more source
irs-partition: An Intrusion Response System utilizing Deep Q-Networks and system partitions
SoftwareX, 2022 Intrusion Response is a relatively new field of research. Recent approaches for the creation of Intrusion Response Systems (IRSs) use Reinforcement Learning (RL) as a primary technique for the optimal or near-optimal selection of the proper ...
Valeria Cardellini +6 more
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Weighted Local Discriminant Preservation Projection Ensemble Algorithm With Embedded Micro-Noise
IEEE Access, 2019 High-dimensional data often cause the “curse of dimensionality” in data processing. Dimensionality reduction can effectively solve the curse of dimensionality and has been widely used in high-dimensional data processing.
Yuchuan Liu +3 more
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An Investigation into the Relationship between Curse of Dimensionality and Dunning-Kruger Effect
Sakarya University Journal of Computer and Information Sciences, 2020 This study addresses a novel perspective for analyzing the source of confidence in human behavior. The concept of confidence was examined via the relationship between two phenomena in the area of machine learning and psychology, namely the Dunning-Kruger
Dr. Mehmet Cem Çatalbaş
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Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality
Theory of Computing, 2012 We present two algorithms for the approximate nearest neighbor problem in high dimensional spaces. For data sets of size n living in IR d , the algorithms require space that is only polynomial in n and d , while achieving query times that are sub-linear ...
Sariel Har-Peled, P. Indyk, R. Motwani
semanticscholar +1 more source
Deep ReLU Networks Overcome the Curse of Dimensionality for Generalized Bandlimited Functions
Journal of Computational Mathematics, 2021 We prove a theorem concerning the approximation of generalized bandlimited multivariate functions by deep ReLU networks for which the curse of the dimensionality is overcome.
Hadrien Montanelli
semanticscholar +1 more source
Detecting disease-associated genomic outcomes using constrained mixture of Bayesian hierarchical models for paired data. [PDF]
PLoS ONE, 2017 Detecting disease-associated genomic outcomes is one of the key steps in precision medicine research. Cutting-edge high-throughput technologies enable researchers to unbiasedly test if genomic outcomes are associated with disease of interest.
Yunfeng Li +6 more
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A Survey on Dimensionality Reduction Techniques for Time-Series Data
IEEE Access, 2023 Data analysis in modern times involves working with large volumes of data, including time-series data. This type of data is characterized by its high dimensionality, enormous volume, and the presence of both noise and redundant features.
Mohsena Ashraf +6 more
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