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IEEE Access, 2021 Dimensionality reduction is a hot research topic in pattern recognition. Traditional dimensionality reduction methods can be separated into linear dimensionality reduction methods and nonlinear dimensionality reduction methods.
Shuzhi Su, Gang Zhu, Yanmin Zhu
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Nonlinear dimensionality reduction on graphs [PDF]
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017 In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated from social, commercial and brain network interactions. Their efficient processing calls for dimensionality reduction techniques capable of properly compressing the data while
Shen, Yanning +2 more
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Nonlinear dimensionality reduction for the acoustic field measured by a linear sensor array [PDF]
MATEC Web of Conferences, 2019 Dimensionality reduction is one of the central problems in machine learning and pattern recognition, which aims to develop a compact representation for complex data from high-dimensional observations.
Zhang Xinyao, Wang Pengyu, Wang Ning
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Temporal nonlinear dimensionality reduction [PDF]
The 2011 International Joint Conference on Neural Networks, 2011 Existing Nonlinear dimensionality reduction (NLDR) algorithms make the assumption that distances between observations are uniformly scaled. Unfortunately, with many interesting systems, this assumption does not hold. We present a new technique called Temporal NLDR (TNLDR), which is specifically designed for analyzing the high-dimensional observations ...
Mike Gashler, Tony Martinez
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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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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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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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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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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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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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