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Haisu: Hierarchically supervised nonlinear dimensionality reduction. [PDF]
PLoS Computational Biology, 2022 We propose a novel strategy for incorporating hierarchical supervised label information into nonlinear dimensionality reduction techniques. Specifically, we extend t-SNE, UMAP, and PHATE to include known or predicted class labels and demonstrate the ...
Kevin Christopher VanHorn +1 more
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Advances in Mathematical Physics, 2021 Dimensionality reduction of images with high-dimensional nonlinear structure is the key to improving the recognition rate. Although some traditional algorithms have achieved some results in the process of dimensionality reduction, they also expose their ...
Yongbin Liu, Jingjie Wang, Wei Bai
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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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Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach [PDF]
Sensors, 2019 Simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are at the forefront of technologies of interest to physicians and scientists because they combine the benefits of both modalities—better time ...
Marek Piorecky +4 more
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Efficient and reliable spike sorting from neural recordings with UMAP-based unsupervised nonlinear dimensionality reduction. [PDF]
PLoS BiologySpike sorting is one of the cornerstones of extracellular electrophysiology. By leveraging advanced signal processing and data analysis techniques, spike sorting makes it possible to detect, isolate, and map single neuron spiking activity from both in ...
Daniel Suárez-Barrera +11 more
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Nonlinear dimensionality reduction and Bayesian optimization for accelerating design of materials [PDF]
Scientific ReportsOptimizing biobased foam formulations is challenging because experiments are costly and fast-to-measure surrogate properties occupy high-dimensional spaces.
Muhammad Osman Nadeem Farooqui +4 more
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Remote Sensing, 2020 Accurate and timely information on the spatial distribution of crops is of great significance to precision agriculture and food security. Many cropland mapping methods using satellite image time series are based on expert knowledge to extract ...
Yongguang Zhai +4 more
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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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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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