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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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ENSO dynamics in current climate models: an investigation using nonlinear dimensionality reduction [PDF]
Nonlinear Processes in Geophysics, 2008 Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality.
I. Ross, P. J. Valdes, S. Wiggins
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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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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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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
Georgios B. Giannakis +2 more
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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 ...
Michael S. Gashler, Tony Martinez
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Stability and dimensionality reduction in nonlinear filtering [PDF]
, 2023 The focus of this thesis is the analysis of the stability and robustness of continuous-time, finite state-space nonlinear filters, in order to provide new and practically relevant quantitative error bounds for a general class of approximate filters. This analysis is carried out through the use of the Hilbert projective metric.
Fausti, Eliana
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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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A biological model of nonlinear dimensionality reduction
2023 Conference on Cognitive Computational Neuroscience, 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
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Nonlinear Dimensionality Reduction Methods in Climate Data Analysis
, 2008 Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality.
Ross, Ian
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