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IMMERSIVE VISUALIZATION OF THE QUALITY OF DIMENSIONALITY REDUCTION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013 Dimensionality reduction is the most widely used approach for extracting the most informative low-dimensional features from highdimensional ones. During the last two decades, different techniques (linear and nonlinear) have been proposed by researchers ...
M. Babaee, M. Datcu, G. Rigoll
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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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Analyzing Grid-Based Direct Quantum Molecular Dynamics Using Non-Linear Dimensionality Reduction
Molecules, 2021 Grid-based schemes for simulating quantum dynamics, such as the multi-configuration time-dependent Hartree (MCTDH) method, provide highly accurate predictions of the coupled nuclear and electronic dynamics in molecular systems.
Gareth W. Richings, Scott Habershon
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Dimensionality reduction for visualizing single-cell data using UMAP
Nature Biotechnology, 2018 Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear
E. Becht +7 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 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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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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Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series [PDF]
, 2007 The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process.
Li, Rui, Sclaroff, Stan, Tian, Tai-Peng
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Nonlinear Dimensionality Reduction for Discriminative Analytics of Multiple Datasets [PDF]
IEEE Transactions on Signal Processing, 2018 Principal component analysis (PCA) is widely used for feature extraction and dimensionality reduction, with documented merits in diverse tasks involving high-dimensional data.
Jia Chen, G. Wang, G. Giannakis
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Generating low-dimensional denoised representations of nonlinear data with superparamagnetic agents [PDF]
, 2014 Copyright ©2016 IEICEVisualisation of high-dimensional data by means of a low-dimensional embedding plays a key role in explorative data analysis.
Christen, Markus +2 more
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