Results 21 to 30 of about 9,313 (254)

A comprehensive survey of anomaly detection techniques for high dimensional big data

Journal of Big Data, 2020
Anomaly detection in high dimensional data is becoming a fundamental research problem that has various applications in the real world. However, many existing anomaly detection techniques fail to retain sufficient accuracy due to so-called “big data ...
Srikanth Thudumu, Philip Branch, Jiong Jin, Jugdutt (Jack) Singh   +3 more
doaj   +1 more source

CNNs Avoid the Curse of Dimensionality by Learning on Patches

IEEE Open Journal of Signal Processing, 2023
Despite the success of convolutional neural networks (CNNs) in numerous computer vision tasks and their extraordinary generalization performances, several attempts to predict the generalization errors of CNNs have only been limited to a posteriori ...
Vamshi C. Madala, Shivkumar Chandrasekaran, Jason Bunk   +2 more
doaj   +1 more source

A survey of band selection techniques for hyperspectral image classification

Journal of Spectral Imaging, 2020
Hyperspectral images usually contain hundreds of contiguous spectral bands, which can precisely discriminate the various spectrally similar classes. However, such high-dimensional data also contain highly correlated and irrelevant information, leading to
Shrutika S. Sawant, Manoharan Prabukumar
doaj   +1 more source

Curse of Dimensionality in Pivot Based Indexes [PDF]

2009 Second International Workshop on Similarity Search and Applications, 2009
We offer a theoretical validation of the curse of dimensionality in the pivot-based indexing of datasets for similarity search, by proving, in the framework of statistical learning, that in high dimensions no pivot-based indexing scheme can essentially outperform the linear scan.
Ilya Volnyansky, Vladimir Pestov
openaire   +2 more sources

High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality

Entropy, 2020
High-dimensional data and high-dimensional representations of reality are inherent features of modern Artificial Intelligence systems and applications of machine learning.
Alexander N. Gorban, Valery A. Makarov, Ivan Y. Tyukin   +2 more
doaj   +1 more source

A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning

The Scientific World Journal, 2014
Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are “trial and error” and “related reward.” A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of ...
Yuchen Fu, Quan Liu, Xionghong Ling, Zhiming Cui   +3 more
doaj   +1 more source

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Advanced Engineering Materials, EarlyView.
A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba, Sang‐Hyeok Lee, Christina Gasper, Martina Freund, Sandra Korte‐Kerzel, Ulrich Kerzel   +5 more
wiley   +1 more source

Limit Theorems as Blessing of Dimensionality: Neural-Oriented Overview

Entropy, 2021
As a system becomes more complex, at first, its description and analysis becomes more complicated. However, a further increase in the system’s complexity often makes this analysis simpler.
Vladik Kreinovich, Olga Kosheleva
doaj   +1 more source

E. coli Extracellular Matrix: A Tunable Composite With Hierarchical Structure

Advanced Materials, EarlyView.
The complex composite‐like mechanical behavior of E. coli biofilm matrix is the result of a synergic contribution of the rigid curli and swelling pEtN‐cellulose, and emerges from specific ratio and assembly conditions. The interactions between the two fibers govern biofilm hydration and characteristic wrinkling patterns, providing crucial insights for ...
Macarena Siri, Agustín Mangiarotti, Anne Seewald, Nikolai Rosenthal, Shahrouz Amini, Emeline Raguin, Peter Fratzl, Cécile M. Bidan   +7 more
wiley   +1 more source

Overcoming the Curse of Dimensionality with Synolitic AI

Technologies
High-dimensional tabular data are common in biomedical and clinical research, yet conventional machine learning methods often struggle in such settings due to data scarcity, feature redundancy, and limited generalization. In this study, we systematically
Alexey Zaikin, Ivan Sviridov, Artem Sosedka, Anastasia Linich, Ruslan Nasyrov, Evgeny M. Mirkes, Tatiana Tyukina   +6 more
doaj   +1 more source