Results 21 to 30 of about 4,379,261 (288)
NNNPE: non-neighbourhood and neighbourhood preserving embedding
Connection Science, 2022 Manifold learning is an important class of methods for nonlinear dimensionality reduction. Among them, the LLE optimisation goal is to maintain the relationship between local neighbourhoods in the original embedding manifold to reduce dimensionality, and
Kaizhi Chen +4 more
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Nonlinear Dimensionality Reduction Based on HSIC Maximization
IEEE Access, 2018 Hilbert-Schmidt independence criterion (HSIC) is typically used to measure the statistical dependence between two sets of data. HSIC first transforms these two sets of data into two reproducing Kernel Hilbert spaces (RKHS), respectively, and then ...
Zhengming Ma +3 more
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Distortion-Free Nonlinear Dimensionality Reduction [PDF]
, 2008 Nonlinear Dimensionality Reduction is an important issue in many machine learning areas where essentially low-dimensional data is nonlinearly embedded in some high-dimensional space. In this paper, we show that the existing Laplacian Eigenmaps method suffers from the distortion problem, and propose a new distortion-free dimensionality reduction method ...
Yangqing Jia +2 more
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Quantum diffusion map for nonlinear dimensionality reduction [PDF]
Physical Review A, 2021 Inspired by random walk on graphs, diffusion map (DM) is a class of unsupervised machine learning that offers automatic identification of low-dimensional data structure hidden in a high-dimensional dataset. In recent years, among its many applications, DM has been successfully applied to discover relevant order parameters in many-body systems, enabling
Apimuk Sornsaeng +3 more
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Learning Neural Representations and Local Embedding for Nonlinear Dimensionality Reduction Mapping
Mathematics, 2021 This work explores neural approximation for nonlinear dimensionality reduction mapping based on internal representations of graph-organized regular data supports.
Sheng-Shiung Wu +3 more
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Parametric nonlinear dimensionality reduction using kernel t-SNE [PDF]
, 2015 Gisbrecht A, Schulz A, Hammer B. Parametric nonlinear dimensionality reduction using kernel t-SNE. Neurocomputing.
Schulz, Alexander ; https://orcid.org/ +2 more
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Array, 2021 High-dimensional data with many features are usually challenging to represent with standard visualization techniques. Usually, one has to resort to dimensionality reduction techniques such as PCA, MDS or t-SNE to represent such data.
Adrien Bibal +3 more
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Nonlinear Dimensionality Reduction for Face Recognition [PDF]
, 2009 Principal component analysis (PCA) has long been a dominating linear technique for dimensionality reduction. Many nonlinear methods and neural networks have been proposed to extend PCA for complex nonlinear data. They include kernel PCA, local linear embedding, isomap, self-organising map (SOM), and visualization induced SOM (ViSOM), a variant of SOM ...
Weilin Huang, Hujun Yin
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Parallel nonlinear dimensionality reduction using GPU acceleration
, 2021 Dimensionality reduction is usually an essential step in data mining and classical machine learning from high-dimensional data. Uniform Manifold Approximations Projections (UMAP) is a recently developed nonlinear dimensionality reduction method that is ...
Qu, Zhonglin (S33642) +3 more
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Improving Dimensionality Reduction Projections for Data Visualization
Applied Sciences, 2023 In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These techniques involve the transformation of high-dimensional data into reduced versions, typically in 2D, with the aim ...
Bardia Rafieian +2 more
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