Results 11 to 20 of about 493,672 (330)
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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A biological model of nonlinear dimensionality reduction [PDF]
Science Advances, 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 ...
Kensuke Yoshida, Taro Toyoizumi
europepmc +4 more sources
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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Nonlinear supervised dimensionality reduction via smooth regular embeddings [PDF]
Pattern Recognition, 2019 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. Meanwhile, existing methods primarily focus on the embedding of the training data, and the generalization of the embedding to initially ...
Cem Örnek, Elif Vural
semanticscholar +5 more sources
A tractable latent variable model for nonlinear dimensionality reduction [PDF]
Proc Natl Acad Sci U S A, 2020 Significance Latent variable models (LVMs) are powerful tools for discovering hidden structure in data. Canonical LVMs include factor analysis, which explains the correlation of a large number of observed variables in terms of a smaller number of ...
L. Saul
semanticscholar +2 more sources
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
openaire +3 more sources
Forward Stepwise Deep Autoencoder-Based Monotone Nonlinear Dimensionality Reduction Methods [PDF]
J Comput Graph Stat, 2020 Dimensionality reduction is an unsupervised learning task aimed at creating a low-dimensional summary and/or extracting the most salient features of a dataset.
Y. Fong, Jun Xu
semanticscholar +2 more sources
Learning Neural Representations and Local Embedding for Nonlinear Dimensionality Reduction Mapping [PDF]
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 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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Linear and Nonlinear Dimensionality Reduction from Fluid Mechanics to Machine Learning [PDF]
Measurement science and technology, 2022 Dimensionality reduction is the essence of many data processing problems, including filtering, data compression, reduced-order modeling and pattern analysis.
Miguel Alfonso Mendez
openalex +3 more sources