4.5 Article

Manifold-based denoising, outlier detection, and dimension reduction algorithm for high-dimensional data

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

Manifold fitting algorithm of noisy manifold data based on variable-scale spectral graph

Tao Yang et al.

Summary: This paper proposes a manifold fitting algorithm for data with noise and manifold structures, which extracts the expected manifold structure to determine the reliability of the data manifold hypothesis and estimates the deviation caused by noise to the manifold structure.

SOFT COMPUTING (2023)

Article Automation & Control Systems

An improved low-frequency noise reduction method in shock wave pressure measurement based on mode classification and recursion extraction

Zhenjian Yao et al.

Summary: The study proposes an improved recursive empirical mode decomposition method for filtering low-frequency noise in shock wave pressure measurements, and uses mode classification and adaptive technology to achieve denoising. The effectiveness of the method is verified through simulations and real experiments, demonstrating its superiority in noise elimination and signal integrity maintenance.

ISA TRANSACTIONS (2021)

Article Computer Science, Artificial Intelligence

Robust Functional Manifold Clustering

Yi Guo et al.

Summary: In this article, the focus is on subspace clustering for functional data or curves and a new robust method to address shift and rotation. Experimental results demonstrate the superiority of this method in clustering functional data.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

Article Biology

Application of noise-reduction techniques to machine learning algorithms for breast cancer tumor identification

Avani Ahuja et al.

Summary: This study examines the application of machine learning techniques in breast cancer diagnosis, showing that noise reduction can improve classification accuracies. By comparing various classification techniques and evaluating noise reduction methods, the study demonstrates that using dimensionality reduction and outlier removal can significantly enhance discrimination accuracies across a wide range of machine learning models.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Article Engineering, Aerospace

Joint parameter and state estimation for stochastic uncertain system with multivariate skew t noises

Shuhui LI et al.

Chinese Journal of Aeronautics (2021)

Article Computer Science, Information Systems

Secure and verifiable outsourced data dimension reduction on dynamic data

Zhenzhu Chen et al.

Summary: The dimensionality reduction aims at reducing redundant information in big data for efficient data analysis. Enterprises or individuals with limited resources often outsource this task to the cloud. However, privacy and security concerns arise due to inadequate supervision. A proposed scheme based on incremental NMF method addresses these concerns while ensuring data confidentiality and verifiability of computation results. Experiment evaluation shows the scheme's high efficiency in saving over 80% computation time for clients.

INFORMATION SCIENCES (2021)

Article Computer Science, Artificial Intelligence

Structure learning with similarity preserving

Zhao Kang et al.

NEURAL NETWORKS (2020)

Article Computer Science, Artificial Intelligence

Flexible unsupervised feature extraction for image classification

Yang Liu et al.

NEURAL NETWORKS (2019)

Article Biotechnology & Applied Microbiology

Dimensionality reduction for visualizing single-cell data using UMAP

Etienne Becht et al.

NATURE BIOTECHNOLOGY (2019)

Article Biotechnology & Applied Microbiology

Visualizing structure and transitions in high-dimensional biological data

Kevin R. Moon et al.

NATURE BIOTECHNOLOGY (2019)

Article Engineering, Electrical & Electronic

Generative Adversarial Networks An overview

Antonia Creswell et al.

IEEE SIGNAL PROCESSING MAGAZINE (2018)

Article Mathematics

Factorization homology of topological manifolds

David Ayala et al.

JOURNAL OF TOPOLOGY (2015)

Proceedings Paper Engineering, Electrical & Electronic

Modeling and Mitigating Noise in Graph and Manifold Representations of Hyperspectral Imagery

Can Jin et al.

ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL IMAGERY XXI (2015)

Article Computer Science, Information Systems

Multi-manifold metric learning for face recognition based on image sets

Likun Huang et al.

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION (2014)

Article Biology

Variable Selection for Clustering with Gaussian Mixture Models

Cathy Maugis et al.

BIOMETRICS (2009)

Article Engineering, Mechanical

Machinery fault diagnosis using supervised manifold learning

Quansheng Jiang et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2009)

Article Computer Science, Theory & Methods

A tutorial on spectral clustering

Ulrike von Luxburg

STATISTICS AND COMPUTING (2007)

Article Mathematics, Applied

Principal manifolds and nonlinear dimensionality reduction via tangent space alignment

ZY Zhang et al.

SIAM JOURNAL ON SCIENTIFIC COMPUTING (2004)

Article Computer Science, Artificial Intelligence

Laplacian eigenmaps for dimensionality reduction and data representation

M Belkin et al.

NEURAL COMPUTATION (2003)

Article Engineering, Multidisciplinary

Control charts for positively-skewed populations with weighted standard deviations

YS Chang et al.

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL (2001)

Article Multidisciplinary Sciences

Nonlinear dimensionality reduction by locally linear embedding

ST Roweis et al.

SCIENCE (2000)

Article Multidisciplinary Sciences

A global geometric framework for nonlinear dimensionality reduction

JB Tenenbaum et al.

SCIENCE (2000)