Results 11 to 20 of about 115,342 (279)
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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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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A biological model of nonlinear dimensionality reduction. [PDF]
Sci Adv, 2023 AbstractObtaining 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 simple ...
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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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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Detecting Adversarial Examples through Nonlinear Dimensionality Reduction [PDF]
, 2019 Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combining non-linear dimensionality reduction and density estimation techniques.
Bacciu, Davide +2 more
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Nonlinear Supervised Dimensionality Reduction via Smooth Regular Embeddings [PDF]
Pattern Recognition, 2018 The recovery of the intrinsic geometric structures of data collections is an important problem in data analysis. Supervised extensions of several manifold learning approaches have been proposed in the recent years.
Ornek, Cem, Vural, Elif
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Research on a multivariate measurement system for polyethylene gas pipelines utilizing a variable weight UMAP model [PDF]
Scientific ReportsIn producing gas-polyethylene pipelines, the five major quality indicators—wall thickness, inner diameter, outer diameter, concentricity, and ovality—exhibit complex interactions, making it challenging for traditional methods to comprehensively evaluate ...
Chenjia Zong +5 more
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Nonlinear dimensionality reduction for clustering [PDF]
Pattern Recognition, 2020 Abstract We introduce an approach to divisive hierarchical clustering that is capable of identifying clusters in nonlinear manifolds. This approach uses the isometric mapping (Isomap) to recursively embed (subsets of) the data in one dimension, and then performs a binary partition designed to avoid the splitting of clusters.
Sotiris Tasoulis +2 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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