Results 11 to 20 of about 26,484 (294)
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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On nonlinear dimensionality reduction for face recognition [PDF]
Image and Vision Computing, 2012 The curse of dimensionality has prompted intensive research in effective methods of mapping high dimensional data. Dimensionality reduction and subspace learning have been studied extensively and widely applied to feature extraction and pattern representation in image and vision applications.
Hujun Yin
exaly +7 more sources
A biological model of nonlinear dimensionality reduction. [PDF]
Sci Adv, 2023 Abstract Obtaining appropriate low-dimensional representations from high-dimensional sensory inputs in an unsupervised manner is essential for straightforward downstream processing. Although nonlinear dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE) have been developed, their implementation in
Yoshida K, Toyoizumi T.
europepmc +3 more sources
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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Nonlinear dimensionality reduction in climate data [PDF]
Nonlinear Processes in Geophysics, 2004 Linear methods of dimensionality reduction are useful tools for handling and interpreting high dimensional data. However, the cumulative variance explained by each of the subspaces in which the data space is decomposed may show a slow convergence that ...
A. J. Gámez +3 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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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 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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Distortion-Free Nonlinear Dimensionality Reduction [PDF]
Lecture Notes in Computer Science, 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
exaly +2 more sources
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
doaj +3 more sources